Data scientist jobs in Rockford, IL

The Senior Data Scientist, Clinical Data Science (HOS & HRA) plays a key role in advancing analytics that improve Medicare Advantage member outcomes and CMS Star Ratings performance. This position supports the design, implementation, and automation of analytic solutions for the Health Outcomes Survey (HOS) and Health Risk Assessment (HRA) programs-two core domains in Aetna's Medicare clinical strategy. The ideal candidate combines strong technical depth in data science and statistical modeling with the ability to translate complex findings into actionable insights for non-technical audiences. This individual will automate recurring data science workflows, conduct robust impact and descriptive analyses, and collaborate closely with clinical, quality, and operations teams to identify emerging opportunities that improve member experience and population health outcomes. Clinical Data Science & Analytics Lead the development of analytic models and descriptive frameworks supporting HOS and HRA performance improvement across Medicare Advantage. Conduct impact analyses, trend identification, and segmentation to explain drivers of performance and inform strategy. Automate recurring analytics and reporting pipelines to increase reliability, efficiency, and reproducibility of insights. Apply advanced statistical, predictive, and causal inference methods to evaluate intervention effectiveness and identify member-level opportunities. Develop and refine tools for data visualization and storytelling to communicate results clearly to non-technical stakeholders. Partner with business leaders to translate analytic results into actionable recommendations for program design, member outreach, and care interventions. Collaboration & Consultation Serve as a bridge between technical and non-technical teams, ensuring analytic outputs are interpretable and actionable. Collaborate cross-functionally with Clinical Operations, Member Experience, and Quality teams to align analytics with enterprise goals. Support enterprise data modernization and automation initiatives by identifying repeatable use cases for scalable analytics and workflow improvement. Mentor junior data scientists and analysts on best practices for data integrity, modeling, and automation. Technical & Operational Excellence Design and maintain automated analytic processes leveraging Python, SQL, and modern cloud environments (e.g., GCP). Ensure accuracy, consistency, and explainability of models and metrics through disciplined version control and validation. Contribute to the team's continuous improvement culture by recommending new methods, tools, or data sources that enhance analytic precision and speed. Required Skills & Experience 5+ years of hands-on experience in data science, advanced analytics, or statistical modeling in healthcare, life sciences, or managed care. Strong proficiency in Python, SQL, and data science libraries (e.g., pandas, scikit-learn, statsmodels). Demonstrated ability to automate data workflows and standardize recurring analyses or reporting. Experience applying statistical and descriptive analytics to clinical or quality measurement problems (e.g., HOS, HRA, CAHPS, or HEDIS). Proven success communicating complex findings to non-technical business partners and influencing decision-making. Ability to work effectively in a fast-paced, cross-functional environment. Nice to Have Skills & Experience Master's or PhD in Data Science, Statistics, Epidemiology, Public Health, or a related quantitative field. Familiarity with Medicare Advantage, CMS Star Ratings methodology, and clinical quality measures. Experience working within modern cloud environments (e.g., Google Cloud Platform, Databricks) and with workflow orchestration tools (Airflow, dbt). Background in impact measurement, causal inference, or time-series analysis in healthcare contexts.

Role: Senior Data Scientist · We are seeking a hands-on Senior Data Scientist to join our Insurance Analytics & AI Vertical. The ideal candidate will bring a blend of insurance domain expertise (preferably P&C), consulting mindset, and strong data science skills. This is a mid-senior level role focused on delivering value through analytics, stakeholder engagement, and logical problem solving, rather than people management. · The role involves working closely with EXL teams and clients on reporting, data engineering, transformation, and advanced analytics projects. While strong technical skills are important, we are looking for someone who can engage directly with clients, translate business needs into analytical solutions, and drive measurable impact. Key Responsibilities · Collaborate with EXL and client stakeholders to design and deliver data-driven solutions across reporting, analytics, and transformation initiatives. · Apply traditional statistical methods, machine learning, deep learning, and NLP techniques to solve business problems. · Support insurance-focused analytics use cases (with preference for P&C lines of business). · Work in a consulting setup: conduct requirement gathering, structure problem statements, and communicate insights effectively to senior stakeholders. · Ensure data quality, governance, and compliance with Data Privacy and Protection Guidelines. · Independently research, analyze, and present findings, ensuring client-ready deliverables. · Contribute to continuous improvement initiatives and support business development activities where required. Key Skillsets & Experience · 7-12 years of experience in analytics, reporting, dashboarding, ETL, Python/R, and associated data management. · Proficiency in machine learning, deep learning algorithms (e.g., neural networks), and text analytics techniques (NLTK, Gensim, LDA, word embeddings like Word2Vec, FastText, GloVe). · Strong consulting background with structured problem-solving and stakeholder management skills. · Excellent communication and presentation skills with the ability to influence and engage senior business leaders. · Hands-on role with ability to independently manage client deliverables and operate in cross-cultural, global environments. Data Management Skills · Strong familiarity with advanced analytics tools (Python, R), BI tools (Tableau, Power BI), and related software applications. · Good knowledge of SQL, Informatica, Hadoop/Spark, ETL tools. · Ability to translate business/functional requirements into technical specifications. · Exposure to cloud data management and AWS services (preferred). Candidate Profile · Bachelor's/Master's degree in Economics, Mathematics, Computer Science/Engineering, Operations Research, or related analytical fields. · Prior insurance industry experience (P&C preferred) strongly desired. · Superior analytical, logical, and problem-solving skills. · Outstanding written and verbal communication abilities with a consultative orientation. · Flexible to work in a fast-paced, evolving environment with occasional visits to the client's Chicago office.

Typical task breakdown: - Assist with monthly reporting on team metrics, cost savings and tariff analysis - Lead development of data analytics to assist category teams in making strategic sourcing decisions Interaction with team: - Will work as a support to multiple category teams Team Structure - Report to the MC&H Strategy Manager and collaborate with Category Managers and buyers Work environment: Office environment Education & Experience Required: - Years of experience: 3-5 - Degree requirement: Bachelors degree - Do you accept internships as job experience: Yes Top 3 Skills · Communicates effectively to develop standard procedures · Applies problem-solving techniques across diverse procurement scenarios Analyzes procurement data to generate actionable insights Additional Technical Skills (Required) - Proficient in PowerBI, PROcure, and tools like CICT, Lognet, MRC, PO Inquiry, AoS - Expertise in Snowflake and data mining (Desired) - Prior experience in Procurement - Familiarity with monthly reporting processes, including ABP (Annual Business Plan) and RBM (Rolling Business Management) - Demonstrated expertise in cost savings initiatives - Machine Learning and AI Soft Skills (Required) - Strong written and verbal communication skills - Balances speed with accuracy in task execution - Defines problems and evaluates their impact (Desired) - Emotional Intelligence - Leadership and team management capabilities

Analyst, Data Analytics | Institutional Real Estate Investment Firm We are working with a leading real estate investment firm that is looking for an Analyst to join their Data Science group, and help play a key role in driving/generating actionable insights across an institutional-grade portfolio of assets, which is part of our client's multibillion-dollar portfolio of diversified real estate holdings throughout the US. This role will combine strategic thinking and advanced analytics to identify opportunities firmwide, and focus on managing data pipelines, creating/refining in-house models, developing controls and workflows, all while engaging with internal and external stakeholders. Our client is an industry leader that continues to raise capital, deploy capital, and outperform its peers year-over-year, and this would be an opportunity to be a part of that growth. Experience: Bachelor's degree in Analytics, Engineering, Statistics, or related, from a top-tier university 3+ years of experience in data engineering, BI, analytics, or related Proficiency in Python, SQL, Power BI, Excel and data visualization Experience employing predictive analytics and forecasting KPIs, in a cross-functional environment If this sounds like you, please apply, and/or reach out to Alex, at ************************.

Job Title: Bigdata Engineer Employment Type: W2 Contract Detailed Job Description: We are seeking a skilled and experienced Big Data Platform Engineer who is having 7+ yrs of experience with a strong background in both development and administration of big data ecosystems. The ideal candidate will be responsible for designing, building, maintaining, and optimizing scalable data platforms that support advanced analytics, machine learning, and real-time data processing. Key Responsibilities: Platform Engineering & Administration: • Install, configure, and manage big data tools such as Hadoop, Spark, Kafka, Hive, HBase, and others. • Monitor cluster performance, troubleshoot issues, and ensure high availability and reliability. • Implement security policies, access controls, and data governance practices. • Manage upgrades, patches, and capacity planning for big data infrastructure. Development & Data Engineering: • Design and develop scalable data pipelines using tools like Apache Spark, Flink, NiFi, or Airflow. • Build ETL/ELT workflows to ingest, transform, and load data from various sources. • Optimize data storage and retrieval for performance and cost-efficiency. • Collaborate with data scientists and analysts to support model deployment and data exploration.

Scaylor helps mid-market and enterprise companies make sense of their data. Most teams past $20M ARR are stuck with fragmented systems, old databases, and hundreds of spreadsheets that never quite line up. We build the pipelines that clean, normalize, and connect that data so it can actually be used. Our platform handles everything from parsing financial models and reports to mapping tables across legacy databases and modern APIs. The goal is simple: give companies a single source of truth they can trust. We're a small team of four - one backend engineer, one frontend engineer, and 2 founders. We're looking for our fifth teammate to help us scale the engine behind it all. ⸻ The Role You'll work across data engineering and full-stack development, helping us build reliable data infrastructure that powers real workflows. You'll touch everything from ingestion and transformation pipelines to the APIs and dashboards that surface insights to clients. You'll work directly with the founding team and help make technical decisions that define the next version of Scaylor's core platform. ⸻ What You'll Work On • Build data pipelines that extract, clean, and standardize information from Excel files, PDFs, APIs, and legacy databases • Design schemas and transformation logic for structured and semi-structured data • Develop and maintain backend APIs (Python/FastAPI or Node/Express) for data access and analytics • Help connect backend services to our frontend dashboards (React, Node.js, or similar) • Set up and maintain AWS infrastructure (Lambda, S3, ECS, CloudFormation) • Collaborate with clients to understand their data problems and design workflows that fix them ⸻ You'd Be Great Here If You • Have 3-6 years of experience in data engineering, backend, or full-stack roles • Write clean, maintainable code in Python + JS • Understand ETL, data normalization, and schema mapping • Have experience with SQL and working with legacy databases or systems • Are comfortable managing cloud services and debugging data pipelines • Enjoy solving messy data problems and care about building things that last ⸻ Nice to Have • Familiarity with GCP or SQL databases • Understanding of enterprise data flows (ERP, CRM, or financial systems) • Experience building and deploying containers (Docker, GitHub Actions, CI/CD) • Interest in lightweight ML or LLM-assisted data transformation ⸻ Why Join Scaylor • Be one of the first five team members shaping the product and the company • Work directly with the founder and help define Scaylor's technical direction • Build infrastructure that solves real problems for real companies • Earn meaningful equity and have a say in how the company grows ⸻ Compensation • $130k - $150k with a raise based on set revenue triggers • .4% equity • Relocation to Chicago, IL required

Data Engineer - Build the Data Engine Behind AI Execution - Starting Salary $150,000 You'll be part architect, part systems designer, part execution partner - someone who thrives at the intersection of engineering precision, scalability, and impact. As the builder behind the AI data platform, you'll turn raw, fragmented data into powerful, reliable systems that feed intelligent products. You'll shape how data flows, how it scales, and how it powers decision-making across AI, analytics, and product teams. Your work won't be behind the scenes - it will be the foundation of everything we build. You'll be joining a company built for builders. Our model combines AI consulting, venture building, and company creation into one execution flywheel. Here, you won't just build data pipelines - you'll build the platforms that power real products and real companies. You know that feeling when a data system scales cleanly under real-world pressure, when latency drops below target, when complexity turns into clarity - and everything just flows? That's exactly what you'll build here. Ready to engineer the platform that powers AI execution? Let's talk. No up-to-date resume required.

Our client is seeking a skilled and experienced Data Analyst professional to join their Product Analytics team. The successful candidate will be responsible for analyzing data sets to identify trends, patterns, and insights to inform business decisions, and ensuring the quality of data analysis. The ideal candidate should have strong analytical skills, a deep understanding of data analytics, and data visualization. Role Type: 6+ Months Contract (Strong possibility of extension or conversion) Location: Chicago, IL Project Name: Digital Technology Product Analytics Overview: Key Responsibilities: • Analyze data to identify trends and issues that could impact business operations and propose solutions to mitigate risks • Ensure data integrity by reviewing data for completeness, accuracy, and consistency •Develop and automate large data sets for the purpose of data mining and analysis - Data mining and analysis for ad hoc projects will include: exploratory data analysis, and data visualization. •Understand data and apply available tools to solve evolving business problems Qualifications: • Bachelor's degree in business, computer science, or a related field • Excellent communication skills, with the ability to communicate complex data insights to different stakeholders •5+ years of experience as Data Analyst, Business Intelligence Developer, or a related analytical role •Willingness to continue to develop/learn new technical skills in support of new tools/methods •Highly experienced with SQL and relational databases, experience with Python is a plus •Proficient with data visualization (Power BI, Tableau, Spotfire, Excel....) •Strong analysis, data manipulation, and problem solving skills •Desire to attack complex data sets •Ability to work independently and manage several simultaneous efforts with a focus on results • Experience with Akamai, Quantum Metrics, and Dynatrace is a big plus Top 5 Skill sets: 1. Highly analytical and excel at ambiguity 2. Highly experienced with SQL - Extensive experience working with large and complex datasets - we have ~400M rows of data per day. 3. Strong knowledge of Quantum Metrics 4. Independent with minimal supervision - self starter 5. We do not get requirements from our users - so this individual will need to be a thought leader and present ideas.

Resource 1 is in need of a Sr. AWS Data Engineer for a full-time/ permanent position with our client in Naperville, IL. Candidate must be local to Illinois because of future hybrid onsite in Naperville expected. Our client is an employee-owned company with excellent benefits, growth opportunities and profit-sharing bonus. This position is focused on building modern data pipelines, integrations and back-end data solutions. Selected individual will work within cross-functional Agile teams, collaborating with product owners, business analysts and other engineers to design and deliver data solutions that power business insights and AI products. Responsibilities: Design and develop scalable data pipelines for ingestion, transformation and integration using AWS services. Pull data from PostgreSQL and SQL Server to migrate to AWS. Create and modify jobs in AWS and modify logic in SQL Server. Create SQL queries, stored procedures and functions in PostgreSQL and RedShift. Provide input on data modeling and schema design as needed. Manage infrastructure through infrastructure-as-code templates (Serverless Framework), supporting new data products and services in AWS. Support inbound/ outbound data flows, including APIs, S3 replication and secured data. Assist with data visualization/ reporting as needed. Follow an Agile development methodology, with regular workshops and standup meetings, working in two-week sprints. Qualifications: 5+ years of data engineering experience. Experience with AWS and its associated array of offerings (Glue, Redshift, Athena, S3, Lambda, Spectrum). Strong experience with SQL, Python and PySpark. A background in supply chain, logistics or distribution would be a plus. Experience with Power BI is a plus.

Our client is currently seeking a Data Analyst Hybrid to the northern suburbs of Chicago W2 Only In this role, you'll evaluate usage, adoption, and performance of IIN systems, identify data mining opportunities, and integrate findings with key performance indicators. You'll collaborate with cross-functional teams to deliver dashboards, reports, and scorecards that inform strategy. What You'll Do Analyze usage, adoption, and efficacy of IIN systems. Identify and execute data mining opportunities. Integrate insights with traditional KPIs. Provide analytical support for projects and stakeholders. Develop and share dashboards, reports, and scorecards. Ensure data accuracy, format, and availability. What We're Looking For Bachelor's degree in Statistics, Data Science, Mathematics, or related field. Strong analytical skills and ability to interpret complex datasets. Advanced Excel skills (pivot tables, VLOOKUP, Power Query, visualization). Expertise in Tableau for dashboard design and optimization. Hands-on experience with Snowflake and SQL for data warehousing and analysis. Strong business acumen and ability to align data strategies with goals. Excellent communication and collaboration skills. Nice to Have Experience with KPI reporting and BI tools. Familiarity with advanced data visualization techniques. Rate: $65-90/HR

D365 F&O Data Analyst - D365 F&O, Dynamics365 F&O, D365 Finance & Operations, Dynamics365 Finance & Operations, D365, Dynamics365, F&SCM, Finance & Supply Chain Management, Data Analyst, Data Lead, Data Modelling, Data Mapping, Data Entities, SQL, ETL, ELT, Azure Data Factory, ADF, SSIS - $100-110ph W2 - Chicago Our Microsoft Partner client is seeking a D365 F&O Data Analyst to assist them with a client project in Illinois. The role will typically involve 3 days per week on-site near Chicago. This role requires strong experience in data analysis and exceptional communication skills to ensure clean, accurate, and timely data migration that adheres to D365 best practices. This is a W2 contract role, C2C is not available. Candidates must be US citizens or green card holders. Responsibilities: Source-to-Target Mapping: Conduct deep analysis of data residing in various legacy systems, defining precise mapping rules to fit the D365 F&O data entities and structures. Data Profiling & Quality: Proactively profile source data to identify gaps, inconsistencies, and quality issues. Collaborate with business owners to define and implement necessary data cleansing rules before migration. Pipeline Construction: Design, build, and maintain robust data extraction, transformation, and loading (ETL) pipelines using tools such as SQL, Azure Data Factory (ADF), SSIS, or Power Query to move data into the D365 staging environments. Validation & Reconciliation: Develop rigorous data validation routines and reconciliation reports to ensure all data is accurately migrated and financial/operational totals match between source and D365. Performance Optimization: Optimize queries and pipelines for efficiency and speed to support multiple migration cycles and large data volumes. Required Skills & Experience: Technical Data Skills: Expert-level proficiency in SQL (writing complex queries, stored procedures, and performing performance tuning). ETL/Pipeline Tools: Proven experience designing and implementing ETL/ELT pipelines using tools like Azure Data Factory, SSIS, or similar platforms. Data Modeling & Analysis: Strong experience in data profiling, data cleansing, and data modeling concepts. Proactive & Independent: Demonstrated ability to independently investigate complex data issues, anticipate risks, and drive solutions forward without continuous direction. Communication: Excellent written and verbal communication skills, with the ability to articulate technical issues and data quality risks clearly to all levels of the organization. Program Experience: Prior experience working within large-scale ERP implementation or system migration programs. Location: Chicago Candidates must be eligible to work in this country. Catch Resource Management is a leading provider of Dynamics 365, JD Edwards, NetSuite, EPM and other ERP resources to both end users and to product suppliers/authors. Our consultants deliver a completely professional resourcing service, always backed up by our team of ERP specialists who are all experienced in full project life cycle implementation and support, thus ensuring that we fully understand our clients' requirements and our candidates' skills. If you have the relevant skills and experience for this position we would welcome your application, however please note that we receive high levels of responses to our advertisements so can only immediately respond to those that are a close match. However, if you are interested in hearing about similar positions then please register on our website: *******************

Primary Location: Itasca, IL Hybrid in Chicago's Northwest Suburbs 2 Days In-Office, 3 Days WFH TYPE: Direct Hire / Permanent Role MUST BE Citizen and Green Card The Data Engineer will be responsible for designing, developing, and maintaining scalable data pipelines and integrations that support data analytics and business intelligence across the organization. This role is essential to ensuring high-quality data delivery, optimizing performance, and enabling effective decision-making through reliable data solutions. What You Bring to the Role (Ideal Experience) Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent work experience). 5+ years of experience as a Data Engineer. 3+ years of experience with the following: Building and supporting data lakehouse architectures using Delta Lake and change data feeds. Working with PySpark and Python, with strong Object-Oriented Programming (OOP) experience to extend existing frameworks. Designing data warehouse table architecture such as star schema or Kimball method. Writing and maintaining versioned Python wheel packages to manage dependencies and distribute code. Creating and managing CI/CD pipelines, especially using Azure DevOps for Microsoft Fabric-related assets. Experience establishing scalable and maintainable data integrations and pipelines in Databricks environments. Nice to Have's Hands-on experience implementing data solutions using Microsoft Fabric. Experience with machine learning/ML and data science tools. Knowledge of data governance and security best practices. Experience in a larger IT environment with 3,000+ users and multiple domains. Current industry certifications from Microsoft cloud/data platforms or equivalent certifications. One or more of the following is preferred: Microsoft Certified: Fabric Data Engineer Associate Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure Data Fundamentals Google Professional Data Engineer Certified Data Management Professional (CDMP) IBM Certified Data Architect - Big Data What You'll Do (Skills Used in this Position) Design and develop scalable data pipelines to collect, process, and store large volumes of structured and unstructured data. Extend and enhance existing OOP-based frameworks developed in Python and PySpark. Partner with data scientists and analysts to define requirements and design robust data analytics solutions. Ensure data quality and integrity through data cleansing, validation, and automated testing procedures. Develop and maintain technical documentation, including requirements, design specifications, and test plans. Implement and manage data integrations from multiple internal and external sources. Optimize data workflows to improve performance, reliability, and reduce cloud consumption. Monitor, troubleshoot, and resolve data pipeline issues to ensure consistent data delivery. Establish and manage CI/CD pipelines and release processes, particularly using Azure DevOps for Microsoft Fabric. Provide technical leadership and coordination for global development and support teams. Participate in creating a safe and healthy workplace by adhering to organizational safety protocols. Support additional projects and initiatives as assigned by management.

We are seeking a highly skilled Data Engineer with strong expertise in Scala, AWS, and Apache Spark. The ideal candidate will have 7+ years of hands-on experience building scalable data pipelines, distributed processing systems, and cloud-native data solutions. Key Responsibilities Design, build, and optimize large-scale data pipelines using Scala and Spark. Develop and maintain ETL/ELT workflows across AWS services. Work on distributed data processing using Spark, Hadoop, or similar. Build data ingestion, transformation, cleansing, and validation routines. Optimize pipeline performance and ensure reliability in production environments. Collaborate with cross-functional teams to understand requirements and deliver robust solutions. Implement CI/CD best practices, testing, and version control. Troubleshoot and resolve issues in complex data flow systems. Required Skills & Experience 7+ years of Data Engineering experience. Strong programming experience with Scala (must-have). Hands-on experience with Apache Spark (core, SQL, streaming). Solid experience with AWS cloud services (Glue, EMR, Lambda, S3, EC2, IAM, etc.). High proficiency in SQL and relational/no SQL data stores. Strong understanding of data modeling, data architecture, and distributed systems. Experience with workflow orchestration tools (Airflow, Step Functions, etc.). Strong communication and problem-solving skills. Preferred Skills Experience with Kafka, Kinesis, or other streaming platforms. Knowledge of containerization tools like Docker or Kubernetes. Background in data warehousing or modern data lake architectures.

MathCo Role - Data/AI Engineering Manager Onsite - Chicago - 4 days in office (Mandatory) Industry - Pharma (Mandatory) As platform architect/owner, you will: Lead the end-to-end architecture, lifecycle, and governance of the AI/Analytics platform, defining standards, reusable components, and integration patterns. Partner with AI/Data architects to enable scalable model deployment and enhance agentic orchestration. Translate business needs into platform features, manage onboarding, documentation, and cross-functional collaboration for platform adoption. Oversee infrastructure-as-code, CI/CD, observability, and containerized environments to ensure reliability and scalability. Evaluate complex technical proposals and develop actionable platform roadmaps and architecture recommendations Stay current on key AI platform developments and assess their impact on architecture and client strategy Coach others, recognize their strengths, and encourage them to take ownership of their personal development Skills Required Experience in designing, architecting, or managing distributed data and AI platforms in cloud environments (AWS, Azure, or GCP) Proven ability to carry out complex Proof of Concept (POC), pilot projects, and limited production rollouts for AI use-cases, focusing on developing new or improved techniques and procedures. Strong skills in pipeline/workflow optimization and data processing frameworks to evaluate architectural choices Years of Experience Minimum of 8 years in relevant experience, preferably with a consulting background and experience with Pharma clients

requires visa independent candidates. Note: (OPT, CPT, H1B holders will not work at this time) Design, develop, and maintain scalable ETL pipelines using AWSGlue Collaborate with data engineers and analysts to understand data requirements Build and manage data extraction, transformation, and loading processes Optimize and troubleshoot existing Glue jobs and workflows Ensure data quality, integrity, and security throughout the ETL process Integrate AWS Glue with other AWS services like S3, Lambda, Redshift, and Step Functions Maintain documentation of data workflows and processes Stay updated with the latest AWS tools and best practices Required Skills Strong hands-on experience with AWS Glue, PySpark, and Python Proficiency in SQL and working with structured/unstructured data (JSON, CSV, Parquet) Experience with data warehousing concepts and tools Familiarity with CI/CD pipelines, Terraform, and scripting (PowerShell, Bash) Solid understanding of data modeling, data integration, and data management Exposure to AWS Batch, Step Functions, and Data Catalogs

Job Title - Junior Data Engineer Duration - Fulltime No of Positions - 8 Interview Process - Imocha test & 1 CG Interview Key Responsibilities Design, develop, and maintain scalable data pipelines and ETL processes using Databricks. Design an develop Python scripts for data transformation, automation, and integration tasks. Develop and optimize SQL queries for data extraction, transformation, and loading. Collaborate with data scientists, analysts, and business stakeholders Ensure data integrity, security, and compliance with organizational standards. Participate in code reviews and contribute to best practices in data engineering Required Skills 3-5 years of professional experience in data engineering or related roles. Strong proficiency in Databricks (including Spark-based data processing). Strong programming skills in Python Advanced knowledge of SQL for querying and data modeling. Familiarity with Azure cloud and ADF Understanding of ETL frameworks, data governance, and performance tuning. Knowledge of CI/CD practices and version control (Git). Exposure to BI tools (Power BI, Tableau) for data visualization Mandatory Skills Python, Databricks, SQL, ETL, Power BI & tableau (good to have) If your Interested. Kindly, share us your resume on **************************** Life At Capgemini Capgemini supports all aspects of your well-being throughout the changing stages of your life and career. For eligible employees, we offer: Flexible work Healthcare including dental, vision, mental health, and well-being programs Financial well-being programs such as 401(k) and Employee Share Ownership Plan Paid time off and paid holidays Paid parental leave Family building benefits like adoption assistance, surrogacy, and cryopreservation Social well-being benefits like subsidized back-up child/elder care and tutoring Mentoring, coaching and learning programs Employee Resource Groups Disaster Relief Disclaimer Capgemini is an Equal Opportunity Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law. This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodations do not pose an undue hardship. Capgemini is committed to providing reasonable accommodations during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact. Click the following link for more information on your rights as an Applicant ************************************************************************** Salary Transparency Capgemini discloses salary range information in compliance with state and local pay transparency obligations. The disclosed range represents the lowest to highest salary we, in good faith, believe we would pay for this role at the time of this posting, although we may ultimately pay more or less than the disclosed range, and the range may be modified in the future. The disclosed range takes into account the wide range of factors that are considered in making compensation decisions including, but not limited to, geographic location, relevant education, qualifications, certifications, experience, skills, seniority, performance, sales or revenue-based metrics, and business or organizational needs. At Capgemini, it is not typical for an individual to be hired at or near the top of the range for their role. The base salary range for the tagged location is $56186 to $87556 /yearly. This role may be eligible for other compensation including variable compensation, bonus, or commission. Full time regular employees are eligible for paid time off, medical/dental/vision insurance, 401(k), and any other benefits to eligible employees. Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, or any other form of compensation that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company's sole discretion, consistent with the law.

Job Title: Data Engineer - Workflow Automation Employment Type: Contract to Hire or Full-Time Department: Project Scion / Information Management Solutions Key Responsibilities: Design, build, and manage workflows using Automic or experience with similar tools like Autosys, Apache Airflow, or Cybermation. workflow orchestration across multi-cloud ecosystems (AWS, Azure, Snowflake, Databricks, Redshift). Monitor and troubleshoot workflow execution, ensuring high availability, reliability, and performance. Administer and maintain workflow platforms. Collaborate with architecture and infrastructure teams to align workflows with cloud strategies. Support migrations, upgrades, and workflow optimization efforts Required Skills: Has 5+ years of experience in IT managing production grade system Hands-on experience with Automic or similar enterprise workflow automation tools. Strong analytical and problem-solving skills. Good communication and documenting skills. Familiarity with cloud platforms and technologies (e.g., AWS, Azure, Snowflake, Databricks). Scripting proficiency (e.g., Shell, Python). Ability to manage workflows across hybrid environments and optimize performance. Experience managing production operations & support activities Preferred Skills: Experience with CI/CD pipeline integration. Knowledge of cloud-native orchestration tools Exposure to monitoring and alerting systems.

Join a dynamic team focused on building innovative data solutions that drive strategic insights for the business. This is an opportunity to leverage your expertise in Snowflake, ETL processes, and data integration. Key Responsibilities Develop Snowflake-based data models to support enterprise-level reporting. Design and implement batch ETL pipelines for efficient data ingestion from legacy systems. Collaborate with stakeholders to gather and understand data requirements. Required Qualifications Hands-on experience with Snowflake for data modeling and schema design. Proven track record in developing ETL pipelines and understanding transformation logic. Solid SQL skills to perform complex data transformations and optimization. If you are passionate about building cutting-edge data solutions and want to make a significant impact, we would love to see your application! #11290

The Data Engineer will design, build, and optimize the data pipelines and models that support the firm's evolving research, analytics, and systematic portfolio construction environment. This role is central to enabling data-driven investment processes, including quantitative research, AI/ML capabilities, and front-office automation. Candidates must have deep expertise with Snowflake, strong SQL skills, and experience integrating diverse datasets used across investment organizations. The role is highly collaborative and requires comfort working in an iterative, fast-moving environment where data needs evolve rapidly based on stakeholder input. Responsibilities Design, build, and enhance ETL/ELT pipelines in Snowflake, ensuring high performance, reliability, and scalability. Integrate internal and external datasets, including pricing, research content, economic releases, market data, and security reference data. Support real-time or near-real-time data flows where needed (e.g., pricing, indicative quotes, market-sensitive inputs). Collaborate closely with Product Leads, Quant Developers, and UI/UX teams to ensure data structures meet the requirements of research workflows, analytical models, and user-facing applications. Partner with front-office stakeholders to rapidly iterate on evolving analytical and data needs. Implement data validation, monitoring, and quality frameworks to ensure accuracy and reliability across critical datasets. Translate prototype pipelines into production-ready workflows with appropriate documentation, standards, and controls. Contribute to data modeling standards, metadata frameworks, and data governance practices across the platform. Requirements 10+ years of data engineering experience within investment management, financial technology, or similar data-intensive environments. Expert-level SQL, including complex queries, schema design, and performance optimization. Deep hands-on experience with Snowflake, including advanced features such as tasks, streams, performance tuning, and secure data sharing. Strong Python capabilities for ETL/ELT development, data processing, and workflow automation. Experience integrating APIs and working with structured, semi-structured, and unstructured datasets. Familiarity with NLP or AI/ML-oriented datasets (e.g., textual research content, PDFs) is a plus. Experience with Domino or willingness to work within a Domino-based model environment. Working knowledge of investment data structures (holdings, benchmarks, pricing, exposures) is highly preferred. Ability to thrive in a rapid prototyping environment with evolving requirements and close partnership with front-office teams.