Data Scientist jobs at Protingent

Kavaliro is seeking a Data Scientist to provide highly technical and in-depth data engineering support. The candidate MUST have experience designing and building data infrastructure, developing data pipelines, transforming and preparing data, ensuring data quality and security, and monitoring and optimizing systems. The candidate MUST have extensive experience with Python and AWS. Experience with SQL, multi-data source queries with database technologies (PostgreSQL, MySQL, RDS, etc.), NiFi, Git, Elasticsearch, Kibana, Jupyter Notebooks, NLP, AI, and any data visualization tools (Tableau, Kibana, Qlik, etc.) are desired. Required Skills and Demonstrated Experience Demonstrated experience with data engineering, to include designing and building data infrastructure, developing data pipelines, transforming/preparing data, ensuring data quality and security, and monitoring/optimizing systems. Demonstrated experience with data management and integration, including designing and perating robust data layers for application development across local and cloud or web data sources. Demonstrated work experience programming with Python Demonstrated experience building scalable ETL and ELT workflows for reporting and analytics. Demonstrated experience with general Linux computing and advanced bash scripting Demonstrated experience with SQL. Demonstrated experience constructing complex multi-data source queries with database technologies such as PostgreSQL, MySQL, Neo4J or RDS Demonstrated experience processing data sources containing structured or unstructured data Demonstrated experience developing data pipelines with NiFi to bring data into a central environment Demonstrated experience delivering results to stakeholders through written documentation and oral briefings Demonstrated experience using code repositories such as Git Demonstrated experience using Elastic and Kibana Demonstrated experience working with multiple stakeholders Demonstrated experience documenting such artifacts as code, Python packages and methodologies Demonstrated experience using Jupyter Notebooks Demonstrated experience with machine learning techniques including natural language processing Demonstrated experience explaining complex technical issues to more junior data scientists, in graphical, verbal, or written formats Demonstrated experience developing tested, reusable and reproducible work Work or educational background in one or more of the following areas: mathematics, statistics, hard sciences (e.g. Physics, Computational Biology, Astronomy, Neuroscience, etc.) computer science, data science, or business analytics Desired Skills and Demonstrated Experience Demonstrated experience with cloud services, such as AWS, as well as cloud data technologies and architecture. Demonstrated experience using big data processing tools such as Apache Spark or Trino Demonstrated experience with machine learning algorithms Demonstrated experience with using container frameworks such as Docker or Kubernetes Demonstrated experience with using data visualizations tools such as Tableau, Kibana or Apache Superset Demonstrated experience creating learning objectives and creating teaching curriculum in technical or scientific fields Location: McLean, Virginia This position is onsite and there is no remote availability. Clearance: TS/SCI with Full Scope Polygraph Applicant MUST hold a permanent U.S. citizenship for this position in accordance with government contract requirements. Kavaliro provides Equal Employment Opportunities to all employees and applicants. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Kavaliro is committed to the full inclusion of all qualified individuals. In keeping with our commitment, Kavaliro will take the steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if reasonable accommodation is required to fully participate in the job application or interview process, to perform the essential functions of the position, and/or to receive all other benefits and privileges of employment, please respond to this posting to connect with a company representative.

Kavaliro is seeking a Data Scientist to provide highly technical and in-depth data engineering support. MUST have experience with Python, PyTorch, Flask (knowledge at minimum with ability to quickly pickup), Familiarity with REST APIs (at minimum), Statistics background/experience, Basic understanding of NLP. Desired skills for a candidate include experience performance R&D with natural language processing, deploying CNN and LLMs or foundational models, deploying ML models on multimedia data, experience with Linux System Administration (or bash), experience with Android Configuration, experience in embedded systems (Raspberry Pi). Required Skills and Demonstrated Experience Demonstrated experience in Python, Javascript, and R. Demonstrated experience employing machine learning and deep learning modules such as Pandas, Scikit, Tensorflow, Pytorch. Demonstrated experience with statistical inference, as well as building and understanding predictive models, using machine learning methods. Demonstrated experience with large-scale text analytics. Desired Skills Demonstrated hands-on experience performing research or development with natural language processing and working with, deploying, and testing Convolutional Neural Networks (CNN), large-language models (LLMs) or foundational models. Demonstrated experience developing and deploying testing and verification methodologies to evaluate algorithm performance and identify strategies for improvement or optimization. Demonstrated experience deploying machine learning models on multimedia data, to include joint text, audio, video, hardware, and peripherals. Demonstrated experience with Linux System Administration and associated scripting languages (Bash) Demonstrated experience with Android configuration, software development, and interfacing. Demonstrated experience in embedded systems (Raspberry Pi) Develops and conducts independent testing and evaluation methods on research-grade algorithms in applicable fields. Reports results and provide documentation and guidance on working with the research-grade algorithms. Evaluates, Integrates and leverage internally-hosted data science tools. Customize research grade algorithms to be optimized for memory and computational efficiency through quantizing, trimming layers, or through custom methods Location: Reston, Virginia This position is onsite and there is no remote availability. Clearance: Active TS/SCI with Full Scope Polygraph Applicant MUST hold a permanent U.S. citizenship for this position in accordance with government contract requirements. Kavaliro provides Equal Employment Opportunities to all employees and applicants. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Kavaliro is committed to the full inclusion of all qualified individuals. In keeping with our commitment, Kavaliro will take the steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if reasonable accommodation is required to fully participate in the job application or interview process, to perform the essential functions of the position, and/or to receive all other benefits and privileges of employment, please respond to this posting to connect with a company representative.

10+years of experience required in Information Technology. Python Programming: At least 5 years of hands-on experience with Python, particularly in frameworks like FastAPI, Django, Flask, and experience using AI frameworks. • Access Control Expertise: Strong understanding of access control models such as Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC). • API and Connector Development: Experience in developing API connectors using Python for extracting and managing access control data from platforms like Azure, SharePoint, Java, .NET, WordPress, etc. • AI and Machine Learning: Hands-on experience integrating AI into applications for automating tasks such as access control reviews and identifying anomalies • Cloud and Microsoft Technologies: Proficiency with Azure services, Microsoft Graph API, and experience integrating Python applications with Azure for access control reviews and reporting. • Reporting and Visualization: Experience using reporting libraries in Python (Pandas, Matplotlib, Plotly, Dash) to build dashboards and reports related to security and access control metrics. • Communication Skills: Ability to collaborate with various stakeholders, explain complex technical solutions, and deliver high-quality solutions on time. • PlainID: Experience or familiarity with PlainID platforms for identity and access management. • Azure OpenAI: Familiarity with Azure OpenAI technologies and their application in access control and security workflows. • Power BI: Experience with Microsoft Power BI for data visualization and reporting. • Agile Methodologies: Experience working in Agile environments and familiarity with Scrum methodologies for delivering security solutions.

Title: Principal Data Scientist Duration: 12 Months Contract Additional Information California Resident Candidates Only. This position is HYBRID (2 days onsite, 2 days telework). Interviews will be conducted via Microsoft Teams. The work schedule follows a 4/40 (10-hour days, Monday-Thursday), with the specific shift determined by the program manager. Shifts may range between 7:15 a.m. and 6:00 p.m. Job description: The Principal Data Scientist works to establish a comprehensive Data Science Program to advance data-driven decision-making, streamline operations, and fully leverage modern platforms including Databricks, or similar, to meet increasing demand for predictive analytics and AI solutions. The Principal Data Scientist will guide program development, provide training and mentorship to junior members of the team, accelerate adoption of advanced analytics, and build internal capacity through structured mentorship. The Principal Data Scientist will possess exceptional communication abilities, both verbal and written, with a strong customer service mindset and the ability to translate complex concepts into clear, actionable insights; strong analytical and business acumen, including foundational experience with regression, association analysis, outlier detection, and core data analysis principles; working knowledge of database design and organization, with the ability to partner effectively with Data Management and Data Engineering teams; outstanding time management and organizational skills, with demonstrated success managing multiple priorities and deliverables in parallel; a highly collaborative work style, coupled with the ability to operate independently, maintain focus, and drive projects forward with minimal oversight; a meticulous approach to quality, ensuring accuracy, reliability, and consistency in all deliverables; and proven mentorship capabilities, including the ability to guide, coach, and upskill junior data scientists and analysts. Experience Required: Five (5)+ years of professional experience leading data science initiatives, including developing machine learning models, statistical analyses, and end-to-end data science workflows in production environments. Three (3)+ years of experience working with Databricks and similar cloud-based analytics platforms, including notebook development, feature engineering, ML model training, and workflow orchestration. Three (3)+ years of experience applying advanced analytics and predictive modeling (e.g., regression, classification, clustering, forecasting, natural language processing). Two (2)+ years of experience implementing MLOps practices, such as model versioning, CI/CD for ML, MLflow, automated pipelines, and model performance monitoring. Two (2)+ years of experience collaborating with data engineering teams to design data pipelines, optimize data transformations, and implement Lakehouse or data warehouse architectures (e.g., Databricks, Snowflake, SQL-based platforms). Two (2)+ years of experience mentoring or supervising junior data scientists or analysts, including code reviews, training, and structured skill development. Two (2)+ years of experience with Python and SQL programming, using data sources such as SQL Server, Oracle, PostgreSQL, or similar relational databases. One (1)+ year of experience operationalizing analytics within enterprise governance frameworks, partnering with Data Management, Security, and IT to ensure compliance, reproducibility, and best practices. Education Required & certifications: This classification requires possession of a Master's degree or higher in Data Science, Statistics, Computer Science, or a closely related field. Additional qualifying professional experience may be substituted for the required education on a year-for-year basis. At least one of the following industry-recognized certifications in data science or cloud analytics, such as: Microsoft Azure Data Scientist Associate (DP-100) Databricks Certified Data Scientist or Machine Learning Professional AWS Machine Learning Specialty Google Professional Data Engineer • or equivalent advanced analytics certifications. The certification is required and may not be substituted with additional experience. About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit ************************ US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. Recruiter Details: Name: T Saketh Ram Sharma Email: ***************************** Internal Id: 25-54101

Title: Senior Data Scientist (Snowflake) For data scientists, additional skill set required to be in AIML, RAG & LLM Models, Agentic AI experience. The Senior Snowflake Data Scientist will lead the development, deployment, and operationalization of machine learning and statistical models that solve complex business problems and drive strategic decision-making. This role requires an expert blend of statistical rigor, advanced programming, and deep knowledge of leveraging Snowflake's ecosystem (e.g., Snowpark, Streamlit, external functions) for high-performance, in-warehouse data science. Key Responsibilities 1. Advanced Modeling & Analysis Model Development: Design, build, train, and validate sophisticated machine learning (ML) and statistical models (e.g., predictive, prescriptive, clustering, forecasting) to address key business challenges (e.g., customer churn, sales forecasting, risk modeling). Feature Engineering: Utilize advanced SQL and Python/Snowpark to perform large-scale feature engineering, data transformation, and preparation directly within Snowflake, ensuring high data quality and low latency for modeling. A/B Testing & Causal Inference: Design and analyze experiments (A/B tests) and employ causal inference techniques to measure the business impact of product features, strategies, and model outputs. 2. MLOps & Production Deployment Operationalization: Lead the process of deploying trained models into production environments, utilizing Snowpark, Snowflake UDFs/UDTFs, and external functions for scalable inference and real-time scoring. Pipeline Automation: Collaborate with Data Engineering to integrate ML pipelines into CI/CD workflows, ensuring models are automatically retrained and redeployed using tools like Airflow or orchestration platforms. Monitoring: Establish and maintain robust monitoring for model performance (drift, bias, accuracy) and operational health within the Snowflake environment. 3. Data Visualization & Storytelling Insight Generation: Conduct deep-dive exploratory data analysis (EDA) using complex Snowflake SQL to uncover hidden patterns, opportunities, and risks. Visualization & Communication: Effectively communicate complex analytical findings, model outputs, and recommendations to technical and non-technical stakeholders and senior leadership using compelling data storytelling and visualization tools (e.g., Tableau, Power BI, or Snowflake Streamlit). 4. Platform & Technical Leadership Best Practices: Define and promote best practices for statistical rigor, ML coding standards, and efficient data processing within the Snowflake ecosystem. Mentorship: Provide technical guidance and mentorship to junior data scientists and analysts on modeling techniques and leveraging Snowflake's data science features. Innovation: Stay current with the latest features of the Snowflake Data Cloud (e.g., Generative AI/LLMs, Unistore, Data Sharing) and propose innovative ways to leverage them for business value. Minimum Qualifications MS or Ph.D. in a quantitative discipline (e.g., Statistics, Computer Science, Engineering, Economics, or Mathematics). 7+ years of progressive experience in Data Science, with at least 3+ years of hands-on experience building and deploying ML solutions in a cloud data warehouse environment, preferably Snowflake. Expert proficiency in Python (including packages like scikit-learn, NumPy, Pandas) and writing scalable code for data processing. Expert-level command of Advanced SQL for complex data manipulation and feature engineering. Proven experience with Machine Learning algorithms and statistical modeling techniques. Strong understanding of MLOps principles for model lifecycle management. Preferred Skills & Certifications Snowflake SnowPro Advanced: Data Scientist Certification. Hands-on experience developing solutions using Snowpark (Python/Scala). Experience building data apps/dashboards using Snowflake Streamlit. Familiarity with cloud platforms and services (AWS Sagemaker, Azure ML, or GCP Vertex AI) integrated with Snowflake. Experience with workflow orchestration tools (e.g., Apache Airflow, dbt)

**NO 3rd Party vendor candidates or sponsorship** Role Title: Senior Data Engineer Client: Global construction and development company Employment Type: Contract Duration: 1 year Preferred Location: Remote based in ET or CT time zones Role Description: The Senior Data Engineer will play a pivotal role in designing, architecting, and optimizing cloud-native data integration and Lakehouse solutions on Azure, with a strong emphasis on Microsoft Fabric adoption, PySpark/Spark-based transformations, and orchestrated pipelines. This role will lead end-to-end data engineering-from ingestion through APIs and Azure services to curated Lakehouse/warehouse layers-while ensuring scalable, secure, well-governed, and well-documented data products. The ideal candidate is hands-on in delivery and also brings data architecture knowledge to help shape patterns, standards, and solution designs. Key Responsibilities Design and implement end-to-end data pipelines and ELT/ETL workflows using Azure Data Factory (ADF), Synapse, and Microsoft Fabric. Build and optimize PySpark/Spark transformations for large-scale processing, applying best practices for performance tuning (partitioning, joins, file sizing, incremental loads). Develop and maintain API-heavy ingestion patterns, including REST/SOAP integrations, authentication/authorization handling, throttling, retries, and robust error handling. Architect scalable ingestion, transformation, and serving solutions using Azure Data Lake / OneLake, Lakehouse patterns (Bronze/Silver/Gold), and data warehouse modeling practices. Implement monitoring, logging, alerting, and operational runbooks for production pipelines; support incident triage and root-cause analysis. Apply governance and security practices across the lifecycle, including access controls, data quality checks, lineage, and compliance requirements. Write complex SQL, develop data models, and enable downstream consumption through analytics tools and curated datasets. Drive engineering standards: reusable patterns, code reviews, documentation, source control, and CI/CD practices. Requirements: Bachelor's degree (or equivalent experience) in Computer Science, Engineering, or a related field. 5+ years of experience in data engineering with strong focus on Azure Cloud. Strong experience with Azure Data Factory pipelines, orchestration patterns, parameterization, and production support. Strong hands-on experience with Synapse (pipelines, SQL pools and/or Spark), and modern cloud data platform patterns. Advanced PySpark/Spark experience for complex transformations and performance optimization. Heavy experience with API-based integrations (building ingestion frameworks, handling auth, pagination, retries, rate limits, and resiliency). Strong knowledge of SQL and data warehousing concepts (dimensional modeling, incremental processing, data quality validation). Strong understanding of cloud data architectures including Data Lake, Lakehouse, and Data Warehouse patterns. Preferred Skills Experience with Microsoft Fabric (Lakehouse/Warehouse/OneLake, Pipelines, Dataflows Gen2, notebooks). Architecture experience (formal or informal), such as contributing to solution designs, reference architectures, integration standards, and platform governance. Experience with DevOps/CI-CD for data engineering using Azure DevOps or GitHub (deployment patterns, code promotion, testing). Experience with Power BI and semantic model considerations for Lakehouse/warehouse-backed reporting. Familiarity with data catalog/governance tooling (e.g., Microsoft Purview).

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work Institute and recognized as a ‘Cool Vendor' and a ‘Vendor to Watch' by Gartner. Please visit Fractal | Intelligence for Imagination for more information about Fractal. Fractal is looking for a proactive and driven AWS Lead Data Architect/Engineer to join our cloud and data tech team. In this role, you will work on designing the system architecture and solution, ensuring the platform is scalable while performant, and creating automated data pipelines. Responsibilities: Design & Architecture of Scalable Data Platforms Design, develop, and maintain large-scale data processing architectures on the Databricks Lakehouse Platform to support business needs Architect multi-layer data models including Bronze (raw), Silver (cleansed), and Gold (curated) layers for various domains (e.g., Retail Execution, Digital Commerce, Logistics, Category Management). Leverage Delta Lake, Unity Catalog, and advanced features of Databricks for governed data sharing, versioning, and reproducibility. Client & Business Stakeholder Engagement Partner with business stakeholders to translate functional requirements into scalable technical solutions. Conduct architecture workshops and solutioning sessions with enterprise IT and business teams to define data-driven use cases Data Pipeline Development & Collaboration Collaborate with data engineers and data scientists to develop end-to-end pipelines using Python, PySpark, SQL Enable data ingestion from diverse sources such as ERP (SAP), POS data, Syndicated Data, CRM, e-commerce platforms, and third-party datasets. Performance, Scalability, and Reliability Optimize Spark jobs for performance tuning, cost efficiency, and scalability by configuring appropriate cluster sizing, caching, and query optimization techniques. Implement monitoring and alerting using Databricks Observability, Ganglia, Cloud-native tools Security, Compliance & Governance Design secure architectures using Unity Catalog, role-based access control (RBAC), encryption, token-based access, and data lineage tools to meet compliance policies. Establish data governance practices including Data Fitness Index, Quality Scores, SLA Monitoring, and Metadata Cataloging. Adoption of AI Copilots & Agentic Development Utilize GitHub Copilot, Databricks Assistant, and other AI code agents for Writing PySpark, SQL, and Python code snippets for data engineering and ML tasks. Generating documentation and test cases to accelerate pipeline development. Interactive debugging and iterative code optimization within notebooks. Advocate for agentic AI workflows that use specialized agents for Data profiling and schema inference. Automated testing and validation. Innovation and Continuous Learning Stay abreast of emerging trends in Lakehouse architectures, Generative AI, and cloud-native tooling. Evaluate and pilot new features from Databricks releases and partner integrations for modern data stack improvements. Requirements: Bachelor's or master's degree in computer science, Information Technology, or a related field. 8-12 years of hands-on experience in data engineering, with at least 5+ years on Python and Apache Spark. Expertise in building high-throughput, low-latency ETL/ELT pipelines on AWS/Azure/GCP using Python, PySpark, SQL. Excellent hands on experience with workload automation tools such as Airflow, Prefect etc. Familiarity with building dynamic ingestion frameworks from structured/unstructured data sources including APIs, flat files, RDBMS, and cloud storage Experience designing Lakehouse architectures with bronze, silver, gold layering. Strong understanding of data modelling concepts, star/snowflake schemas, dimensional modelling, and modern cloud-based data warehousing. Experience with designing Data marts using Cloud data warehouses and integrating with BI tools (Power BI, Tableau, etc.). Experience CI/CD pipelines using tools such as AWS Code commit, Azure DevOps, GitHub Actions. Knowledge of infrastructure-as-code (Terraform, ARM templates) for provisioning platform resources In-depth experience with AWS Cloud services such as Glue, S3, Redshift etc. Strong understanding of data privacy, access controls, and governance best practices. Experience working with RBAC, tokenization, and data classification frameworks Excellent communication skills for stakeholder interaction, solution presentations, and team coordination. Proven experience leading or mentoring global, cross-functional teams across multiple time zones and engagements. Ability to work independently in agile or hybrid delivery models, while guiding junior engineers and ensuring solution quality Must be able to work in PST time zone. Pay: The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $150k - $180k. In addition, you may be eligible for a discretionary bonus for the current performance period. Benefits: As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation. Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work Institute and recognized as a ‘Cool Vendor' and a ‘Vendor to Watch' by Gartner. Please visit Fractal | Intelligence for Imagination for more information about Fractal. Fractal is looking for a proactive and driven AWS Lead Data Architect/Engineer to join our cloud and data tech team. In this role, you will work on designing the system architecture and solution, ensuring the platform is scalable while performant, and creating automated data pipelines. Responsibilities: Design & Architecture of Scalable Data Platforms Design, develop, and maintain large-scale data processing architectures on the Databricks Lakehouse Platform to support business needs Architect multi-layer data models including Bronze (raw), Silver (cleansed), and Gold (curated) layers for various domains (e.g., Retail Execution, Digital Commerce, Logistics, Category Management). Leverage Delta Lake, Unity Catalog, and advanced features of Databricks for governed data sharing, versioning, and reproducibility. Client & Business Stakeholder Engagement Partner with business stakeholders to translate functional requirements into scalable technical solutions. Conduct architecture workshops and solutioning sessions with enterprise IT and business teams to define data-driven use cases Data Pipeline Development & Collaboration Collaborate with data engineers and data scientists to develop end-to-end pipelines using Python, PySpark, SQL Enable data ingestion from diverse sources such as ERP (SAP), POS data, Syndicated Data, CRM, e-commerce platforms, and third-party datasets. Performance, Scalability, and Reliability Optimize Spark jobs for performance tuning, cost efficiency, and scalability by configuring appropriate cluster sizing, caching, and query optimization techniques. Implement monitoring and alerting using Databricks Observability, Ganglia, Cloud-native tools Security, Compliance & Governance Design secure architectures using Unity Catalog, role-based access control (RBAC), encryption, token-based access, and data lineage tools to meet compliance policies. Establish data governance practices including Data Fitness Index, Quality Scores, SLA Monitoring, and Metadata Cataloging. Adoption of AI Copilots & Agentic Development Utilize GitHub Copilot, Databricks Assistant, and other AI code agents for Writing PySpark, SQL, and Python code snippets for data engineering and ML tasks. Generating documentation and test cases to accelerate pipeline development. Interactive debugging and iterative code optimization within notebooks. Advocate for agentic AI workflows that use specialized agents for Data profiling and schema inference. Automated testing and validation. Innovation and Continuous Learning Stay abreast of emerging trends in Lakehouse architectures, Generative AI, and cloud-native tooling. Evaluate and pilot new features from Databricks releases and partner integrations for modern data stack improvements. Requirements: Bachelor's or master's degree in computer science, Information Technology, or a related field. 8-12 years of hands-on experience in data engineering, with at least 5+ years on Python and Apache Spark. Expertise in building high-throughput, low-latency ETL/ELT pipelines on AWS/Azure/GCP using Python, PySpark, SQL. Excellent hands on experience with workload automation tools such as Airflow, Prefect etc. Familiarity with building dynamic ingestion frameworks from structured/unstructured data sources including APIs, flat files, RDBMS, and cloud storage Experience designing Lakehouse architectures with bronze, silver, gold layering. Strong understanding of data modelling concepts, star/snowflake schemas, dimensional modelling, and modern cloud-based data warehousing. Experience with designing Data marts using Cloud data warehouses and integrating with BI tools (Power BI, Tableau, etc.). Experience CI/CD pipelines using tools such as AWS Code commit, Azure DevOps, GitHub Actions. Knowledge of infrastructure-as-code (Terraform, ARM templates) for provisioning platform resources In-depth experience with AWS Cloud services such as Glue, S3, Redshift etc. Strong understanding of data privacy, access controls, and governance best practices. Experience working with RBAC, tokenization, and data classification frameworks Excellent communication skills for stakeholder interaction, solution presentations, and team coordination. Proven experience leading or mentoring global, cross-functional teams across multiple time zones and engagements. Ability to work independently in agile or hybrid delivery models, while guiding junior engineers and ensuring solution quality Must be able to work in PST time zone. Pay: The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $150k - $180k. In addition, you may be eligible for a discretionary bonus for the current performance period. Benefits: As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation. Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work Institute and recognized as a ‘Cool Vendor' and a ‘Vendor to Watch' by Gartner. Please visit Fractal | Intelligence for Imagination for more information about Fractal. Fractal is looking for a proactive and driven AWS Lead Data Architect/Engineer to join our cloud and data tech team. In this role, you will work on designing the system architecture and solution, ensuring the platform is scalable while performant, and creating automated data pipelines. Responsibilities: Design & Architecture of Scalable Data Platforms Design, develop, and maintain large-scale data processing architectures on the Databricks Lakehouse Platform to support business needs Architect multi-layer data models including Bronze (raw), Silver (cleansed), and Gold (curated) layers for various domains (e.g., Retail Execution, Digital Commerce, Logistics, Category Management). Leverage Delta Lake, Unity Catalog, and advanced features of Databricks for governed data sharing, versioning, and reproducibility. Client & Business Stakeholder Engagement Partner with business stakeholders to translate functional requirements into scalable technical solutions. Conduct architecture workshops and solutioning sessions with enterprise IT and business teams to define data-driven use cases Data Pipeline Development & Collaboration Collaborate with data engineers and data scientists to develop end-to-end pipelines using Python, PySpark, SQL Enable data ingestion from diverse sources such as ERP (SAP), POS data, Syndicated Data, CRM, e-commerce platforms, and third-party datasets. Performance, Scalability, and Reliability Optimize Spark jobs for performance tuning, cost efficiency, and scalability by configuring appropriate cluster sizing, caching, and query optimization techniques. Implement monitoring and alerting using Databricks Observability, Ganglia, Cloud-native tools Security, Compliance & Governance Design secure architectures using Unity Catalog, role-based access control (RBAC), encryption, token-based access, and data lineage tools to meet compliance policies. Establish data governance practices including Data Fitness Index, Quality Scores, SLA Monitoring, and Metadata Cataloging. Adoption of AI Copilots & Agentic Development Utilize GitHub Copilot, Databricks Assistant, and other AI code agents for Writing PySpark, SQL, and Python code snippets for data engineering and ML tasks. Generating documentation and test cases to accelerate pipeline development. Interactive debugging and iterative code optimization within notebooks. Advocate for agentic AI workflows that use specialized agents for Data profiling and schema inference. Automated testing and validation. Innovation and Continuous Learning Stay abreast of emerging trends in Lakehouse architectures, Generative AI, and cloud-native tooling. Evaluate and pilot new features from Databricks releases and partner integrations for modern data stack improvements. Requirements: Bachelor's or master's degree in computer science, Information Technology, or a related field. 8-12 years of hands-on experience in data engineering, with at least 5+ years on Python and Apache Spark. Expertise in building high-throughput, low-latency ETL/ELT pipelines on AWS/Azure/GCP using Python, PySpark, SQL. Excellent hands on experience with workload automation tools such as Airflow, Prefect etc. Familiarity with building dynamic ingestion frameworks from structured/unstructured data sources including APIs, flat files, RDBMS, and cloud storage Experience designing Lakehouse architectures with bronze, silver, gold layering. Strong understanding of data modelling concepts, star/snowflake schemas, dimensional modelling, and modern cloud-based data warehousing. Experience with designing Data marts using Cloud data warehouses and integrating with BI tools (Power BI, Tableau, etc.). Experience CI/CD pipelines using tools such as AWS Code commit, Azure DevOps, GitHub Actions. Knowledge of infrastructure-as-code (Terraform, ARM templates) for provisioning platform resources In-depth experience with AWS Cloud services such as Glue, S3, Redshift etc. Strong understanding of data privacy, access controls, and governance best practices. Experience working with RBAC, tokenization, and data classification frameworks Excellent communication skills for stakeholder interaction, solution presentations, and team coordination. Proven experience leading or mentoring global, cross-functional teams across multiple time zones and engagements. Ability to work independently in agile or hybrid delivery models, while guiding junior engineers and ensuring solution quality Must be able to work in PST time zone. Pay: The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $150k - $180k. In addition, you may be eligible for a discretionary bonus for the current performance period. Benefits: As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation. Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work Institute and recognized as a ‘Cool Vendor' and a ‘Vendor to Watch' by Gartner. Please visit Fractal | Intelligence for Imagination for more information about Fractal. Fractal is looking for a proactive and driven AWS Lead Data Architect/Engineer to join our cloud and data tech team. In this role, you will work on designing the system architecture and solution, ensuring the platform is scalable while performant, and creating automated data pipelines. Responsibilities: Design & Architecture of Scalable Data Platforms Design, develop, and maintain large-scale data processing architectures on the Databricks Lakehouse Platform to support business needs Architect multi-layer data models including Bronze (raw), Silver (cleansed), and Gold (curated) layers for various domains (e.g., Retail Execution, Digital Commerce, Logistics, Category Management). Leverage Delta Lake, Unity Catalog, and advanced features of Databricks for governed data sharing, versioning, and reproducibility. Client & Business Stakeholder Engagement Partner with business stakeholders to translate functional requirements into scalable technical solutions. Conduct architecture workshops and solutioning sessions with enterprise IT and business teams to define data-driven use cases Data Pipeline Development & Collaboration Collaborate with data engineers and data scientists to develop end-to-end pipelines using Python, PySpark, SQL Enable data ingestion from diverse sources such as ERP (SAP), POS data, Syndicated Data, CRM, e-commerce platforms, and third-party datasets. Performance, Scalability, and Reliability Optimize Spark jobs for performance tuning, cost efficiency, and scalability by configuring appropriate cluster sizing, caching, and query optimization techniques. Implement monitoring and alerting using Databricks Observability, Ganglia, Cloud-native tools Security, Compliance & Governance Design secure architectures using Unity Catalog, role-based access control (RBAC), encryption, token-based access, and data lineage tools to meet compliance policies. Establish data governance practices including Data Fitness Index, Quality Scores, SLA Monitoring, and Metadata Cataloging. Adoption of AI Copilots & Agentic Development Utilize GitHub Copilot, Databricks Assistant, and other AI code agents for Writing PySpark, SQL, and Python code snippets for data engineering and ML tasks. Generating documentation and test cases to accelerate pipeline development. Interactive debugging and iterative code optimization within notebooks. Advocate for agentic AI workflows that use specialized agents for Data profiling and schema inference. Automated testing and validation. Innovation and Continuous Learning Stay abreast of emerging trends in Lakehouse architectures, Generative AI, and cloud-native tooling. Evaluate and pilot new features from Databricks releases and partner integrations for modern data stack improvements. Requirements: Bachelor's or master's degree in computer science, Information Technology, or a related field. 8-12 years of hands-on experience in data engineering, with at least 5+ years on Python and Apache Spark. Expertise in building high-throughput, low-latency ETL/ELT pipelines on AWS/Azure/GCP using Python, PySpark, SQL. Excellent hands on experience with workload automation tools such as Airflow, Prefect etc. Familiarity with building dynamic ingestion frameworks from structured/unstructured data sources including APIs, flat files, RDBMS, and cloud storage Experience designing Lakehouse architectures with bronze, silver, gold layering. Strong understanding of data modelling concepts, star/snowflake schemas, dimensional modelling, and modern cloud-based data warehousing. Experience with designing Data marts using Cloud data warehouses and integrating with BI tools (Power BI, Tableau, etc.). Experience CI/CD pipelines using tools such as AWS Code commit, Azure DevOps, GitHub Actions. Knowledge of infrastructure-as-code (Terraform, ARM templates) for provisioning platform resources In-depth experience with AWS Cloud services such as Glue, S3, Redshift etc. Strong understanding of data privacy, access controls, and governance best practices. Experience working with RBAC, tokenization, and data classification frameworks Excellent communication skills for stakeholder interaction, solution presentations, and team coordination. Proven experience leading or mentoring global, cross-functional teams across multiple time zones and engagements. Ability to work independently in agile or hybrid delivery models, while guiding junior engineers and ensuring solution quality Must be able to work in PST time zone. Pay: The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $150k - $180k. In addition, you may be eligible for a discretionary bonus for the current performance period. Benefits: As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation. Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Role: Data Engineer Type: Contract-to-Hire or Full-Time Domain: Finance preferred, not mandatory Key Skills & Requirements: Must-have: Strong Python and Informatica experience AI/ML Exposure: Familiarity with building and fine-tuning models; not a heavy AI/ML developer role ETL & Data Engineering: Hands-on experience with ETL pipelines, data modeling, and “data plumbing” Nice-to-have: Snowflake ecosystem experience, HR systems exposure Ability to collaborate with business teams handling Data Science Candidate Profile: Hands-on Data Engineer comfortable building data pipelines and models Exposure to AI/ML concepts without being a full AI specialist Finance domain experience is a plus

KPI Partners is a 5 times Gartner-recognized data, analytics, and AI consulting company. We are leaders in data engineering on Azure, AWS, Google, Snowflake, and Databricks. Founded in 2006, KPI has over 400 consultants and has successfully delivered over 1,000 projects to our clients. We are looking for skilled data engineers who want to work with the best team in data engineering. Title: Senior Data Architect Location: Plano, TX (Hybrid) Job Type: Contract - 6 Months Key Skills: SQL, PySpark, Databricks, and Azure Cloud Key Note: Looking for a Data Architect who is Hands-on with SQL, PySpark, Databricks, and Azure Cloud. About the Role: We are seeking a highly skilled and experienced Senior Data Architect to join our dynamic team at KPI, working on challenging and multi-year data transformation projects. This is an excellent opportunity for a talented data engineer to play a key role in building innovative data solutions using Azure Native Services and related technologies. If you are passionate about working with large-scale data systems and enjoy solving complex engineering problems, this role is for you. Key Responsibilities: Data Engineering: Design, development, and implementation of data pipelines and solutions using PySpark, SQL, and related technologies. Collaboration: Work closely with cross-functional teams to understand business requirements and translate them into robust data solutions. Data Warehousing: Design and implement data warehousing solutions, ensuring scalability, performance, and reliability. Continuous Learning: Stay up to date with modern technologies and trends in data engineering and apply them to improve our data platform. Mentorship: Provide guidance and mentorship to junior data engineers, ensuring best practices in coding, design, and development. Must-Have Skills & Qualifications: Minimum 12+ years of overall experience in IT Industry. 4+ years of experience in data engineering, with a strong background in building large-scale data solutions. 4+ years of hands-on experience developing and implementing data pipelines using Azure stack experience (Azure, ADF, Databricks, Functions) Proven expertise in SQL for querying, manipulating, and analyzing large datasets. Strong knowledge of ETL processes and data warehousing fundamentals. Self-motivated and independent, with a “let's get this done” mindset and the ability to thrive in a fast-paced and dynamic environment. Good-to-Have Skills: Databricks Certification is a plus. Data Modeling, Azure Architect Certification.

Our client is seeking a Senior Data Analytics Engineer (Customer Data) to join their team! This position is located in remote. Build, optimize, and maintain customer data pipelines in PySpark/Databricks to support CDP-driven use cases across AWS/Azure/GCP Transform raw and integrated customer data into analytics-ready datasets used for dashboards, reporting, segmentation, personalization, and downstream AI/ML applications Develop and enrich customer behavior metrics, campaign analytics, and performance insights such as: ad engagement, lifecycle metrics, retention Partner with Marketing, Sales, Product, and Data Science teams to translate business goals into metrics, features, and analytical data models Build datasets consumed by Power BI/Tableau dashboards (hands-on dashboard creation not required) Ensure high cluster performance and pipeline optimization in Databricks, including troubleshooting skewed joins, sorting, partitioning, and real-time processing needs Work across multiple cloud and vendor ecosystems such as: AWS/Azure/GCP; Hightouch or comparable CDP vendors Participate in the data ingestion and digestion phases, shaping integrated data into analytical layers for MarTech and BI Contribute to and enforce data engineering standards, documentation, governance, and best practices across the organization Desired Skills/Experience: 6+ years of experience in Data Engineering, Analytics Engineering, or related fields, including data modeling experience Strong Data Engineering fundamentals with the ability to design pipelines, optimize performance, and deliver real-time or near-real-time datasets Ability to deeply understand data, identifying gaps, designing meaningful transformations, and creating metrics with clear business context Understanding of how customer data moves through Customer Data Platforms (CDPs) and how to design pipelines that integrate with them Experience supporting Marketing, Customer Data, MarTech, CDP, segmentation, or personalization teams strongly preferred Hands-on experience required with: Databricks, PySpark, Python, SQL, Building analytics datasets for dashboards/reporting and customer behavior analytics or campaign performance insights Experience designing and implementing features that feed downstream AI or customer-facing applications Benefits: Medical, Dental, & Vision Insurance Plans Employee-Owned Profit Sharing (ESOP) 401K offered The approximate pay range for this position starting at $150-160,000+. Please note that the pay range provided is a good faith estimate. Final compensation may vary based on factors including but not limited to background, knowledge, skills, and location. We comply with local wage minimums.

We are looking for a seasoned Azure Data Engineer to design, build, and optimize secure, scalable, and high-performance data solutions within the Microsoft Azure ecosystem. This will be a multi-year contract worked FULLY ONSITE in Austin, TX. The ideal candidate brings deep technical expertise in data architecture, ETL/ELT engineering, data integration, and governance, along with hands-on experience in MDM, API Management, Lakehouse architectures, and data mesh or data hub frameworks. This position combines strategic architectural planning with practical, hands-on implementation, empowering cross-functional teams to leverage data as a key organizational asset. Key Responsibilities 1. Data Architecture & Strategy Design and deploy end-to-end Azure data platforms using Azure Data Lake, Azure Synapse Analytics, Azure Databricks, and Azure SQL Database. Build and implement Lakehouse and medallion (Bronze/Silver/Gold) architectures for scalable and modular data processing. Define and support data mesh and data hub patterns to promote domain-driven design and federated governance. Establish standards for conceptual, logical, and physical data modeling across data warehouse and data lake environments. 2. Data Integration & Pipeline Development Develop and maintain ETL/ELT pipelines using Azure Data Factory, Synapse Pipelines, and Databricks for both batch and streaming workloads. Integrate diverse data sources (on-prem, cloud, SaaS, APIs) into a unified Azure data environment. Optimize pipelines for cost-effectiveness, performance, and scalability. 3. Master Data Management (MDM) & Data Governance Implement MDM solutions using Azure-native or third-party platforms (e.g., Profisee, Informatica, Semarchy). Define and manage data governance, metadata, and data quality frameworks. Partner with business teams to align data standards and maintain data integrity across domains. 4. API Management & Integration Build and manage APIs for data access, transformation, and system integration using Azure API Management and Logic Apps. Design secure, reliable data services for internal and external consumers. Automate workflows and system integrations using Azure Functions, Logic Apps, and Power Automate. 5. Database & Platform Administration Perform core DBA tasks, including performance tuning, query optimization, indexing, and backup/recovery for Azure SQL and Synapse. Monitor and optimize cost, performance, and scalability across Azure data services. Implement CI/CD and Infrastructure-as-Code (IaC) solutions using Azure DevOps, Terraform, or Bicep. 6. Collaboration & Leadership Work closely with data scientists, analysts, business stakeholders, and application teams to deliver high-value data solutions. Mentor junior engineers and define best practices for coding, data modeling, and solution design. Contribute to enterprise-wide data strategy and roadmap development. Required Qualifications Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related fields. 5+ years of hands-on experience in Azure-based data engineering and architecture. Strong proficiency with the following: Azure Data Factory, Azure Synapse, Azure Databricks, Azure Data Lake Storage Gen2 SQL, Python, PySpark, PowerShell Azure API Management and Logic Apps Solid understanding of data modeling approaches (3NF, dimensional modeling, Data Vault, star/snowflake schemas). Proven experience with Lakehouse/medallion architectures and data mesh/data hub designs. Familiarity with MDM concepts, data governance frameworks, and metadata management. Experience with automation, data-focused CI/CD, and IaC. Thorough understanding of Azure security, RBAC, Key Vault, and core networking principles. What We Offer Competitive compensation and benefits package Luna Data Solutions, Inc. (LDS) provides equal employment opportunities to all employees. All applicants will be considered for employment. LDS prohibits discrimination and harassment of any type regarding age, race, color, religion, sexual orientation, gender identity, sex, national origin, genetics, protected veteran status, and/or disability status.

Our client is seeking a Big Data Engineer to join their team! This position is located in Santa Monica, California. Design and build core components of a large-scale data platform for both real-time and batch processing, owning key features of big data applications that evolve with business needs Develop next-generation, cloud-based big data infrastructure supporting batch and streaming workloads, with continuous improvements to performance, scalability, reliability, and availability Champion engineering excellence, promoting best practices such as design patterns, CI/CD, thorough code reviews, and automated testing Drive innovation, contributing new ideas and applying cutting-edge technologies to deliver impactful solutions Participate in the full software development lifecycle, including system design, experimentation, implementation, deployment, and testing Collaborate closely with program managers, product managers, SDETs, and researchers in an open, agile, and highly innovative environment Desired Skills/Experience: Bachelor's degree in a STEM field such as: Science, Technology, Engineering, Mathematics 5+ years of relevant professional experience 4+ years of professional software development experience using Java, Scala, Python, or similar programming languages 3+ years of hands-on big data development experience with technologies such as Spark, Flink, SingleStore, Kafka, NiFi, and AWS big data tools Strong understanding of system and application design, architecture principles, and distributed system fundamentals Proven experience building highly available, scalable, and production-grade services Genuine passion for technology, with the ability to work across interdisciplinary areas and adopt new tools or approaches Experience processing massive datasets at the petabyte scale Proficiency with cloud infrastructure and DevOps tools, such as Terraform, Kubernetes (K8s), Spinnaker, IAM, and ALB Hands-on experience with modern data warehousing and analytics platforms, including ClickHouse, Druid, Snowflake, Impala, Presto, Kinesis, and more Familiarity with common web development frameworks, such as Spring Boot, React.js, Vue.js, or Angular Benefits: Medical, Dental, & Vision Insurance Plans Employee-Owned Profit Sharing (ESOP) 401K offered The approximate pay range for this position is between $52.00 and $75.00. Please note that the pay range provided is a good faith estimate. Final compensation may vary based on factors including but not limited to background, knowledge, skills, and location. We comply with local wage minimums.

Our client is seeking a Senior Data Engineer to join their team! This position is located in Glendale, California. Contribute to maintaining, updating, and expanding existing Core Data platform data pipelines Build tools and services to support data discovery, lineage, governance, and privacy Collaborate with other software and data engineers and cross-functional teams Work with a tech stack that includes Airflow, Spark, Databricks, Delta Lake, Kubernetes, and AWS Collaborate with product managers, architects, and other engineers to drive the success of the Core Data platform Contribute to developing and documenting internal and external standards and best practices for pipeline configurations, naming conventions, and more Ensure high operational efficiency and quality of Core Data platform datasets to meet SLAs and ensure reliability and accuracy for stakeholders in Engineering, Data Science, Operations, and Analytics Participate in agile and scrum ceremonies to collaborate and refine team processes Engage with customers to build relationships, understand needs, and prioritize both innovative solutions and incremental platform improvements Maintain detailed documentation of work and changes to support data quality and data governance requirements Desired Skills/Experience: 5+ years of data engineering experience developing large data pipelines Proficiency in at least one major programming language such as: Python, Java or Scala Strong SQL skills and the ability to create queries to analyze complex datasets Hands-on production experience with distributed processing systems such as Spark Experience interacting with and ingesting data efficiently from API data sources Experience coding with the Spark DataFrame API to create data engineering workflows in Databricks Hands-on production experience with data pipeline orchestration systems such as Airflow for creating and maintaining data pipelines Experience developing APIs with GraphQL Deep understanding of AWS or other cloud providers, as well as infrastructure-as-code Familiarity with data modeling techniques and data warehousing best practices Strong algorithmic problem-solving skills Excellent written and verbal communication skills Advanced understanding of OLTP versus OLAP environments Benefits: Medical, Dental, & Vision Insurance Plans Employee-Owned Profit Sharing (ESOP) 401K offered The approximate pay range for this position is between $51.00 and $73.00. Please note that the pay range provided is a good faith estimate. Final compensation may vary based on factors including but not limited to background, knowledge, skills, and location. We comply with local wage minimums.

Our client is seeking an Azure Data Engineer to join their team! This position is located in Irving, Texas. THIS ROLE REQUIRES AN ONSITE INTERVIEW IN IRVING, please only apply if you are local and available to interview onsite. Duties: Lead the design, architecture, and implementation of key data initiatives and platform capabilities Optimize existing data workflows and systems to improve performance, cost-efficiency, identifying and guiding teams to implement solutions Lead and mentor a team of 2-5 data engineers, providing guidance on technical best practices, career development, and initiative execution Contribute to the development of data engineering standards, processes, and documentation, promoting consistency and maintainability across teams while enabling business stakeholders Desired Skills/Experience: Bachelor's degree or equivalent in Computer Science, Mathematics, Software Engineering, Management Information Systems, etc. 5+ years of relevant work experience in data engineering Strong technical skills in SQL, PySpark/Python, Azure, and Databricks Deep understanding of data engineering fundamentals, including database architecture and design, ETL, etc. Benefits: Medical, Dental, & Vision Insurance Plans Employee-Owned Profit Sharing (ESOP) 401K offered The approximate pay range for this position starting at $140-145,000+. Please note that the pay range provided is a good faith estimate. Final compensation may vary based on factors including but not limited to background, knowledge, skills, and location. We comply with local wage minimums.

About the Role We are seeking a highly skilled Databricks Data Engineer with strong expertise in modern data engineering, Azure cloud technologies, and Lakehouse architectures. This role is ideal for someone who thrives in dynamic environments, enjoys solving complex data challenges, and can lead end-to-end delivery of scalable data solutions. What We're Looking For 8+ years designing and delivering scalable data pipelines in modern data platforms Deep experience in data engineering, data warehousing, and enterprise-grade solution delivery Ability to lead cross-functional initiatives in matrixed teams Advanced skills in SQL, Python, and ETL/ELT development, including performance tuning Hands-on experience with Azure, Snowflake, and Databricks, including system integrations Key Responsibilities Design, build, and optimize large-scale data pipelines on the Databricks Lakehouse platform Modernize and enhance cloud-based data ecosystems on Azure, contributing to architecture, modeling, security, and CI/CD Use Apache Airflow and similar tools for workflow automation and orchestration Work with financial or regulated datasets while ensuring strong compliance and governance Drive best practices in data quality, lineage, cataloging, and metadata management Primary Technical Skills Develop and optimize ETL/ELT pipelines using Python, PySpark, Spark SQL, and Databricks Notebooks Design efficient Delta Lake models for reliability and performance Implement and manage Unity Catalog for governance, RBAC, lineage, and secure data sharing Build reusable frameworks using Databricks Workflows, Repos, and Delta Live Tables Create scalable ingestion pipelines for APIs, databases, files, streaming sources, and MDM systems Automate ingestion and workflows using Python and REST APIs Support downstream analytics for BI, data science, and application workloads Write optimized SQL/T-SQL queries, stored procedures, and curated datasets Automate DevOps workflows, testing pipelines, and workspace configurations Additional Skills Azure: Data Factory, Data Lake, Key Vault, Logic Apps, Functions CI/CD: Azure DevOps Orchestration: Apache Airflow (plus) Streaming: Delta Live Tables MDM: Profisee (nice-to-have) Databases: SQL Server, Cosmos DB Soft Skills Strong analytical and problem-solving mindset Excellent communication and cross-team collaboration Detail-oriented with a high sense of ownership and accountability

Responsibilities: Design, build, and maintain scalable data pipelines using AWS Glue and Databricks. Develop and optimize ETL/ELT processes using PySpark and Python. Collaborate with data scientists, analysts, and stakeholders to enable efficient data access and transformation. Implement and maintain data lake and warehouse solutions on AWS (S3, Glue Catalog, Redshift, Athena, etc.). Ensure data quality, consistency, and reliability across systems. Optimize performance of large-scale distributed data processing workflows. Develop automation scripts and frameworks for data ingestion, transformation, and validation. Follow best practices for data governance, security, and compliance. Required Skills & Experience: 5-8 years of hands-on experience in Data Engineering. Strong proficiency in Python and PySpark for data processing and transformation. Expertise in AWS services - particularly Glue, S3, Lambda, Redshift, and Athena. Hands-on experience with Databricks for building and managing data pipelines. Experience working with large-scale data systems and optimizing performance. Solid understanding of data modeling, data lake architecture, and ETL design principles. Strong problem-solving skills and ability to work independently in a fast-paced environment. “Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of - Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”