Data scientist jobs in Rohnert Park, CA

Staff Data Scientist | San Francisco | $250K-$300K + Equity We're partnering with one of the fastest-growing AI companies in the world to hire a Staff Data Scientist. Backed by over $230M from top-tier investors and already valued at over $1B, they've secured customers that include some of the most recognizable names in tech. Their AI platform powers millions of daily interactions and is quickly becoming the enterprise standard for conversational AI. In this role, you'll bring rigorous analytics and experimentation leadership that directly shapes product strategy and company performance. What you'll do: Drive deep-dive analyses on user behavior, product performance, and growth drivers Design and interpret A/B tests to measure product impact at scale Build scalable data models, pipelines, and dashboards for company-wide use Partner with Product and Engineering to embed experimentation best practices Evaluate ML models, ensuring business relevance, performance, and trade-off clarity What we're looking for: 5+ years in data science or product analytics at scale (consumer or marketplace preferred) Advanced SQL and Python skills, with strong foundations in statistics and experimental design Proven record of designing, running, and analyzing large-scale experiments Ability to analyze and reason about ML models (classification, recommendation, LLMs) Strong communicator with a track record of influencing cross-functional teams If you're excited by the sound of this challenge- apply today and we'll be in touch.

We're working with a Series A health tech start-up pioneering a revolutionary approach to healthcare AI, developing neurosymbolic systems that combine statistical learning with structured medical knowledge. Their technology is being adopted by leading health systems and insurers to enhance patient outcomes through advanced predictive analytics. We're seeking Machine Learning Engineers who excel at the intersection of data science, modeling, and software engineering. You'll design and implement models that extract insights from longitudinal healthcare data, balancing analytical rigor, interpretability, and scalability. This role offers a unique opportunity to tackle foundational modeling challenges in healthcare, where your contributions will directly influence clinical, actuarial, and policy decisions. Key Responsibilities Develop predictive models to forecast disease progression, healthcare utilization, and costs using temporal clinical data (claims, EHR, laboratory results, pharmacy records) Design interpretable and explainable ML solutions that earn the trust of clinicians, actuaries, and healthcare decision-makers Research and prototype innovative approaches leveraging both classical and modern machine learning techniques Build robust, scalable ML pipelines for training, validation, and deployment in distributed computing environments Collaborate cross-functionally with data engineers, clinicians, and product teams to ensure models address real-world healthcare needs Communicate findings and methodologies effectively through visualizations, documentation, and technical presentations Required Qualifications Strong foundation in statistical modeling, machine learning, or data science, with preference for experience in temporal or longitudinal data analysis Proficiency in Python and ML frameworks (PyTorch, JAX, NumPyro, PyMC, etc.) Proven track record of transitioning models from research prototypes to production systems Experience with probabilistic methods, survival analysis, or Bayesian inference (highly valued) Bonus Qualifications Experience working with clinical data and healthcare terminologies (ICD, CPT, SNOMED CT, LOINC) Background in actuarial modeling, claims forecasting, or risk adjustment methodologies

Requirements 7 years of experience working as a GenAI Data Science. Experience with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.) Experience with Bedrock, JumpStart, HuggingFace Experience evaluating ethical implications of AI and controlling for them (e.g., red-teaming) Expertise in supervised learning and unsupervised learning along with experience in deep learning and transfer learning Experience in generative algorithms (e.g., GAN, VAE, etc.) as well as pre-trained models (e.g., LLaMa, SAM, etc.) Experience developing models from inception to deployment

🌎 San Francisco (Hybrid) 💼 Founding/Staff Data Engineer 💵 $200-300k base Our client is an elite applied AI research and product lab building AI-native systems for finance-and pushing frontier models into real production environments. Their work sits at the intersection of data, research, and high-stakes financial decision-making. As the Founding Data Engineer, you will own the data platform that powers everything: models, experiments, and user-facing products relied on by demanding financial customers. You'll make foundational architectural decisions, work directly with researchers and product engineers, and help define how data is built, trusted, and scaled from day one. What you'll do: Design and build the core data platform, ingesting, transforming, and serving large-scale financial and alternative datasets. Partner closely with researchers and ML engineers to ship production-grade data and feature pipelines that power cutting-edge models. Establish data quality, observability, lineage, and reproducibility across both experimentation and production workloads. Deploy and operate data services using Docker and Kubernetes in a modern cloud environment (AWS, GCP, or Azure). Make foundational choices on tooling, architecture, and best practices that will define how data works across the company. Continuously simplify and evolve systems-rewriting pipelines or infrastructure when it's the right long-term decision. Ideal candidate: Have owned or built high-performance data systems end-to-end, directly supporting production applications and ML models. Are strongest in backend and data infrastructure, with enough frontend literacy to integrate cleanly with web products when needed. Can design and evolve backend services and pipelines (Node.js or Python) to support new product features and research workflows. Are an expert in at least one statically typed language, with a strong bias toward type safety, correctness, and maintainable systems. Have deployed data workloads and services using Docker and Kubernetes on a major cloud provider. Are comfortable making hard calls-simplifying, refactoring, or rebuilding legacy pipelines when quality and scalability demand it. Use AI tools to accelerate your work, but rigorously review and validate AI-generated code, insisting on sound system design. Thrive in a high-bar, high-ownership environment with other exceptional engineers. Love deep technical problems in data infrastructure, distributed systems, and performance. Nice to have: Experience working with financial data (market, risk, portfolio, transactional, or alternative datasets). Familiarity with ML infrastructure, such as feature stores, experiment tracking, or model serving systems. Background in a high-growth startup or a foundational infrastructure role. Compensation & setup: Competitive salary and founder-level equity Hybrid role based in San Francisco, with close collaboration and significant ownership Small, elite team building core infrastructure with outsized impact

Member of Technical Staff - AI Data Engineer San Francisco (In-Office) $150K to $225K + Equity A high-growth, AI-native startup coming out of stealth is hiring AI Data Engineers to build the systems that power production-grade AI. The company has recently signed a Series A term sheet and is scaling rapidly. This role is central to unblocking current bottlenecks across data engineering, context modeling, and agent performance. Responsibilities: • Build distributed, reliable data pipelines using Airflow, Temporal, and n8n • Model SQL, vector, and NoSQL databases (Postgres, Qdrant, etc.) • Build API and function-based services in Python • Develop custom automations (Playwright, Stagehand, Zapier) • Work with AI researchers to define and expose context as services • Identify gaps in data quality and drive changes to upstream processes • Ship fast, iterate, and own outcomes end-to-end Required Experience: • Strong background in data engineering • Hands-on experience working with LLMs or LLM-powered applications • Data modeling skills across SQL and vector databases • Experience building distributed systems • Experience with Airflow, Temporal, n8n, or similar workflow engines • Python experience (API/services) • Startup mindset and bias toward rapid execution Nice To Have: • Experience with stream processing (Flink) • dbt or Clickhouse experience • CDC pipelines • Experience with context construction, RAG, or agent workflows • Analytical tooling (Posthog) What You Can Expect: • High-intensity, in-office environment • Fast decision-making and rapid shipping cycles • Real ownership over architecture and outcomes • Opportunity to work on AI systems operating at meaningful scale • Competitive compensation package • Meals provided plus full medical, dental, and vision benefits If this sounds like you, please apply now.

The Company: A data services company based in the heart of San Francisco, are looking for a Senior Data Engineer. They are a team of passionate engineers and data experts that are working on a variety of different project, primarily in the financial services sector, helping organizations build scalable, modern data platforms. This is a hands-on, full-time role with close collaboration alongside the CTO and senior engineers, offering strong influence over technical direction and delivery. The Role: This is an on-site position in the downtown San Francisco where you will be working as part of a close-knit team, collaborating on projects in their brand new office. You will be working across end-to-end data projects, including: Building and maintaining data pipelines and ETL processes. Sourcing and integrating third-party APIs and datasets. Batch and near-real-time processing (cloud agnostic). Downstream analytics and reporting using tools like Sigma Computing and Omnium Analytics. Collaborating with the CTO and engineering team to deliver client solutions. Key Skills: 5+ years' data engineering experience Strong Python, BigQuery, and cloud (GCP or similar) Solid ETL and pipeline background Comfortable with large-scale data Nice to Have Beam, Dataflow, Spark, or Hadoop Tableau or Looker ML/AI exposure Kafka or Pub/Sub Given the varied nature of the work, a broad range of technology experience is valued. You don't need to have experience with every tool listed below to be considered, so we encourage you to apply. This role is 5 days a week on-site in downtown San Francisco. Looking to pay between $170,000-$220,000 with a bonus between 15-20%. Benefits Health, Dental & Vision covered Unlimited PTO 401(k) with employer contribution Commuter benefits.

Midjourney is a research lab exploring new mediums to expand the imaginative powers of the human species. We are a small, self-funded team focused on design, human infrastructure, and AI. We have no investors, no big company controlling us, and no advertisers. We are 100% supported by our amazing community. Our tools are already used by millions of people to dream, to explore, and to create. But this is just the start. We think the story of the 2020s is about building the tools that will remake the world for the next century. We're making those tools, to expand what it means to be human. Core Responsibilities: Design and maintain data pipelines to consolidate information across multiple sources (subscription platforms, payment systems, infrastructure and usage monitoring, and financial systems) into a unified analytics environment Build and manage interactive dashboards and self-service BI tools that enable leadership to track key business metrics including revenue performance, infrastructure costs, customer retention, and operational efficiency Serve as technical owner of our financial planning platform (Pigment or similar), leading implementation and build-out of models, data connections, and workflows in partnership with Finance leadership to translate business requirements into functional system architecture Develop automated data quality checks and cleaning processes to ensure accuracy and consistency across financial and operational datasets Partner with Finance, Product and Operations teams to translate business questions into analytical frameworks, including cohort analysis, cost modeling, and performance trending Create and maintain documentation for data models, ETL processes, dashboard logic, and system workflows to ensure knowledge continuity Support strategic planning initiatives by building financial models, scenario analyses, and data-driven recommendations for resource allocation and growth investments Required Qualifications: 3-5+ years experience in data engineering, analytics engineering, or similar role with demonstrated ability to work with large-scale datasets Strong SQL skills and experience with modern data warehousing solutions (BigQuery, Snowflake, Redshift, etc.) Proficiency in at least one programming language (Python, R) for data manipulation and analysis Experience with BI/visualization tools (Looker, Tableau, Power BI, or similar) Hands-on experience administering enterprise financial systems (NetSuite, SAP, Oracle, or similar ERP platforms) Experience working with Stripe Billing or similar subscription management platforms, including data extraction and revenue reporting Ability to communicate technical concepts clearly to non-technical stakeholders

If you're hands on with modern data platforms, cloud tech, and big data tools and you like building solutions that are secure, repeatable, and fast, this role is for you. As a Senior Data Engineer, you will design, build, and maintain scalable data pipelines that transform raw information into actionable insights. The ideal candidate will have strong experience across modern data platforms, cloud environments, and big data technologies, with a focus on building secure, repeatable, and high-performing solutions. Responsibilities: Design, develop, and maintain secure, scalable data pipelines to ingest, transform, and deliver curated data into the Common Data Platform (CDP). Participate in Agile rituals and contribute to delivery within the Scaled Agile Framework (SAFe). Ensure quality and reliability of data products through automation, monitoring, and proactive issue resolution. Deploy alerting and auto-remediation for pipelines and data stores to maximize system availability. Apply a security first and automation-driven approach to all data engineering practices. Collaborate with cross-functional teams (data scientists, analysts, product managers, and business stakeholders) to align infrastructure with evolving data needs. Stay current on industry trends and emerging tools, recommending improvements to strengthen efficiency and scalability. Qualifications: Bachelor's degree in Computer Science, Information Systems, or related field (or equivalent experience). At least 3 years of experience with Python and PySpark, including Jupyter notebooks and unit testing. At least 2 years of experience with Databricks, Collibra, and Starburst. Proven work with relational and NoSQL databases, including STAR and dimensional modeling approaches. Hands-on experience with modern data stacks: object stores (S3), Spark, Airflow, lakehouse architectures, and cloud warehouses (Snowflake, Redshift). Strong background in ETL and big data engineering (on-prem and cloud). Work within enterprise cloud platforms (CFS2, Cloud Foundational Services 2/EDS) for governance and compliance. Experience building end-to-end pipelines for structured, semi-structured, and unstructured data using Spark.

Citizenship Requirement: U.S. Citizens Only ITTConnect is seeking a Data Engineer / Analytics to work for one of our clients, a major Technology Consulting firm with headquarters in Europe. They are experts in tailored technology consulting and services to banks, investment firms and other Financial vertical clients. Job location: San Francisco Bay area or NY City. Work Model: Ability to come into the office as requested Seniority: 10+ years of total experience About the role: The Data Engineer / Analytics Specialist will support analytics, product insights, and AI initiatives. You will build robust data pipelines, integrate data sources, and enhance the organization's analytical foundations. Responsibilities: Build and operate Snowflake-based analytics environments. Develop ETL/ELT pipelines (DBT, Airflow, etc.). Integrate APIs, external data sources, and streaming inputs. Perform query optimization, basic data modeling, and analytics support. Enable downstream GenAI and analytics use cases. Requirements: 10+ years of overall technology experience 3+ years hands-on AWS experience required Strong SQL and Snowflake experience. Hands-on pipeline engineering with DBT, Airflow, or similar. Experience with API integrations and modern data architectures.

Job Title: Data Engineer - Retail / E-Commerce 🏢 Company: Aaratech Inc 🛑 Eligibility: Only U.S. Citizens and Green Card holders are eligible. Please note that we do not offer visa sponsorship. Aaratech Inc. is seeking a results-driven Data Engineer - Retail / E-Commerce to support customer, sales, and product data platforms. The role focuses on building scalable pipelines that enable real-time and batch analytics for business growth. Key Responsibilities: 🔹 Data Pipeline Development Develop and maintain data pipelines for sales, customer, and product data. Integrate data from e-commerce platforms and marketing systems. 🔹 Data Modeling Design data models to support analytics and BI reporting. Optimize performance and scalability. 🔹 Data Quality Ensure data accuracy, completeness, and consistency. Implement monitoring and error-handling processes. 🔹 Collaboration Work closely with data analysts, product, and marketing teams. 🔹 Tools & Technologies Use SQL, Python, ETL tools, and cloud data platforms. Qualifications: ✅ Bachelor's degree in Computer Science, Engineering, or related field ✅ Minimum 2 years of Data Engineering experience ✅ Strong SQL and Python skills ✅ Experience with data pipelines and analytics platforms ✅ Retail or e-commerce data experience preferred ✅ Strong problem-solving skills

About us: Intuitive is an innovation-led engineering company delivering business outcomes for 100's of Enterprises globally. With the reputation of being a Tiger Team & a Trusted Partner of enterprise technology leaders, we help solve the most complex Digital Transformation challenges across following Intuitive Superpowers: Modernization & Migration Application & Database Modernization Platform Engineering (IaC/EaC, DevSecOps & SRE) Cloud Native Engineering, Migration to Cloud, VMware Exit FinOps Data & AI/ML Data (Cloud Native / DataBricks / Snowflake) Machine Learning, AI/GenAI Cybersecurity Infrastructure Security Application Security Data Security AI/Model Security SDx & Digital Workspace (M365, G-suite) SDDC, SD-WAN, SDN, NetSec, Wireless/Mobility Email, Collaboration, Directory Services, Shared Files Services Intuitive Services: Professional and Advisory Services Elastic Engineering Services Managed Services Talent Acquisition & Platform Resell Services About the job: Title: Imaging Data Engineer/Architect Start Date: Immediate # of Positions: 1 Position Type: Contract/ Full-Time Location: San Francisco, CA Notes: Imaging data Engineer/architect who understands Radiology and Digital pathology, related clinical data and metadata. Hands-on experience on above technologies, and with good knowledge in the biomedical imaging, and data pipelines overall. About the Role We are seeking a highly skilled Imaging Data Engineer/Architect to join our San Francisco team as a Subject Matter Expert (SME) in radiology and digital pathology. This role will design and manage imaging data pipelines, ensuring seamless integration of clinical data and metadata to support advanced diagnostic and research applications. The ideal candidate will have deep expertise in medical imaging standards, cloud-based data architectures, and healthcare interoperability, contributing to innovative solutions that enhance patient outcomes. Responsibilities Design and implement scalable data architectures for radiology and digital pathology imaging data, including DICOM, HL7, and FHIR standards. Develop and optimize data pipelines to process and store large-scale imaging datasets (e.g., MRI, CT, histopathology slides) and associated metadata. Collaborate with clinical teams to understand radiology and pathology workflows, ensuring data solutions align with clinical needs. Ensure data integrity, security, and compliance with healthcare regulations (e.g., HIPAA, GDPR). Integrate imaging data with AI/ML models for diagnostic and predictive analytics, working closely with data scientists. Build and maintain metadata schemas to support data discoverability and interoperability across systems. Provide technical expertise to cross-functional teams, including product managers and software engineers, to drive imaging data strategy. Conduct performance tuning and optimization of imaging data storage and retrieval systems in cloud environments (e.g., AWS, Google Cloud, Azure). Document data architectures and processes, ensuring knowledge transfer to internal teams and external partners. Stay updated on emerging imaging technologies and standards, proposing innovative solutions to enhance data workflows. Qualifications Education: Bachelor's degree in computer science, Biomedical Engineering, or a related field (master's preferred). Experience: 5+ years in data engineering or architecture, with at least 3 years focused on medical imaging (radiology and/or digital pathology). Proven experience with DICOM, HL7, FHIR, and imaging metadata standards (e.g., SNOMED, LOINC). Hands-on experience with cloud platforms (AWS, Google Cloud, or Azure) for imaging data storage and processing. Technical Skills: Proficiency in programming languages (e.g., Python, Java, SQL) for data pipeline development. Expertise in ETL processes, data warehousing, and database management (e.g., Snowflake, BigQuery, PostgreSQL). Familiarity with AI/ML integration for imaging data analytics. Knowledge of containerization (e.g., Docker, Kubernetes) for deploying data solutions. Domain Knowledge: Deep understanding of radiology and digital pathology workflows, including PACS and LIS systems. Familiarity with clinical data integration and healthcare interoperability standards. Soft Skills: Strong analytical and problem-solving skills to address complex data challenges. Excellent communication skills to collaborate with clinical and technical stakeholders. Ability to work independently in a fast-paced environment, with a proactive approach to innovation. Certifications (preferred): AWS Certified Solutions Architect, Google Cloud Professional Data Engineer, or equivalent. Certifications in medical imaging (e.g., CIIP - Certified Imaging Informatics Professional).

Salary: $200-250k base + RSUs This fast-growing Series E AI SaaS company is redefining how modern engineering teams build and deploy applications. We're expanding our data science organization to accelerate customer success after the initial sale-driving onboarding, retention, expansion, and long-term revenue growth. About the Role As the senior data scientist supporting post-sales teams, you will use advanced analytics, experimentation, and predictive modeling to guide strategy across Customer Success, Account Management, and Renewals. Your insights will help leadership forecast expansion, reduce churn, and identify the levers that unlock sustainable net revenue retention. Key Responsibilities Forecast & Model Growth: Build predictive models for renewal likelihood, expansion potential, churn risk, and customer health scoring. Optimize the Customer Journey: Analyze onboarding flows, product adoption patterns, and usage signals to improve activation, engagement, and time-to-value. Experimentation & Causal Analysis: Design and evaluate experiments (A/B tests, uplift modeling) to measure the impact of onboarding programs, success initiatives, and pricing changes on retention and expansion. Revenue Insights: Partner with Customer Success and Sales to identify high-value accounts, cross-sell opportunities, and early warning signs of churn. Cross-Functional Partnership: Collaborate with Product, RevOps, Finance, and Marketing to align post-sales strategies with company growth goals. Data Infrastructure Collaboration: Work with Analytics Engineering to define data requirements, maintain data quality, and enable self-serve dashboards for Success and Finance teams. Executive Storytelling: Present clear, actionable recommendations to senior leadership that translate complex analysis into strategic decisions. About You Experience: 6+ years in data science or advanced analytics, with a focus on post-sales, customer success, or retention analytics in a B2B SaaS environment. Technical Skills: Expert SQL and proficiency in Python or R for statistical modeling, forecasting, and machine learning. Domain Knowledge: Deep understanding of SaaS metrics such as net revenue retention (NRR), gross churn, expansion ARR, and customer health scoring. Analytical Rigor: Strong background in experimentation design, causal inference, and predictive modeling to inform customer-lifecycle strategy. Communication: Exceptional ability to translate data into compelling narratives for executives and cross-functional stakeholders. Business Impact: Demonstrated success improving onboarding efficiency, retention rates, or expansion revenue through data-driven initiatives.

Staff Data Scientist | San Francisco | $250K-$300K + Equity We're partnering with one of the fastest-growing AI companies in the world to hire a Staff Data Scientist. Backed by over $230M from top-tier investors and already valued at over $1B, they've secured customers that include some of the most recognizable names in tech. Their AI platform powers millions of daily interactions and is quickly becoming the enterprise standard for conversational AI. In this role, you'll bring rigorous analytics and experimentation leadership that directly shapes product strategy and company performance. What you'll do: Drive deep-dive analyses on user behavior, product performance, and growth drivers Design and interpret A/B tests to measure product impact at scale Build scalable data models, pipelines, and dashboards for company-wide use Partner with Product and Engineering to embed experimentation best practices Evaluate ML models, ensuring business relevance, performance, and trade-off clarity What we're looking for: 5+ years in data science or product analytics at scale (consumer or marketplace preferred) Advanced SQL and Python skills, with strong foundations in statistics and experimental design Proven record of designing, running, and analyzing large-scale experiments Ability to analyze and reason about ML models (classification, recommendation, LLMs) Strong communicator with a track record of influencing cross-functional teams If you're excited by the sound of this challenge- apply today and we'll be in touch.

Requirements 7 years of experience working as a GenAI Data Science. Experience with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.) Experience with Bedrock, JumpStart, HuggingFace Experience evaluating ethical implications of AI and controlling for them (e.g., red-teaming) Expertise in supervised learning and unsupervised learning along with experience in deep learning and transfer learning Experience in generative algorithms (e.g., GAN, VAE, etc.) as well as pre-trained models (e.g., LLaMa, SAM, etc.) Experience developing models from inception to deployment

Member of Technical Staff - AI Data Engineer San Francisco (In-Office) $150K to $225K + Equity A high-growth, AI-native startup coming out of stealth is hiring AI Data Engineers to build the systems that power production-grade AI. The company has recently signed a Series A term sheet and is scaling rapidly. This role is central to unblocking current bottlenecks across data engineering, context modeling, and agent performance. Responsibilities: • Build distributed, reliable data pipelines using Airflow, Temporal, and n8n • Model SQL, vector, and NoSQL databases (Postgres, Qdrant, etc.) • Build API and function-based services in Python • Develop custom automations (Playwright, Stagehand, Zapier) • Work with AI researchers to define and expose context as services • Identify gaps in data quality and drive changes to upstream processes • Ship fast, iterate, and own outcomes end-to-end Required Experience: • Strong background in data engineering • Hands-on experience working with LLMs or LLM-powered applications • Data modeling skills across SQL and vector databases • Experience building distributed systems • Experience with Airflow, Temporal, n8n, or similar workflow engines • Python experience (API/services) • Startup mindset and bias toward rapid execution Nice To Have: • Experience with stream processing (Flink) • dbt or Clickhouse experience • CDC pipelines • Experience with context construction, RAG, or agent workflows • Analytical tooling (Posthog) What You Can Expect: • High-intensity, in-office environment • Fast decision-making and rapid shipping cycles • Real ownership over architecture and outcomes • Opportunity to work on AI systems operating at meaningful scale • Competitive compensation package • Meals provided plus full medical, dental, and vision benefits If this sounds like you, please apply now.

The Company: A data services company based in the heart of San Francisco, are looking for a Senior Data Engineer. They are a team of passionate engineers and data experts that are working on a variety of different project, primarily in the financial services sector, helping organizations build scalable, modern data platforms. This is a hands-on, full-time role with close collaboration alongside the CTO and senior engineers, offering strong influence over technical direction and delivery. The Role: This is an on-site position in the downtown San Francisco where you will be working as part of a close-knit team, collaborating on projects in their brand new office. You will be working across end-to-end data projects, including: Building and maintaining data pipelines and ETL processes. Sourcing and integrating third-party APIs and datasets. Batch and near-real-time processing (cloud agnostic). Downstream analytics and reporting using tools like Sigma Computing and Omnium Analytics. Collaborating with the CTO and engineering team to deliver client solutions. Key Skills: 5+ years' data engineering experience Strong Python, BigQuery, and cloud (GCP or similar) Solid ETL and pipeline background Comfortable with large-scale data Nice to Have Beam, Dataflow, Spark, or Hadoop Tableau or Looker ML/AI exposure Kafka or Pub/Sub Given the varied nature of the work, a broad range of technology experience is valued. You don't need to have experience with every tool listed below to be considered, so we encourage you to apply. This role is 5 days a week on-site in downtown San Francisco. Looking to pay between $170,000-$220,000 with a bonus between 15-20%. Benefits Health, Dental & Vision covered Unlimited PTO 401(k) with employer contribution Commuter benefits.

Citizenship Requirement: U.S. Citizens Only ITTConnect is seeking a Data Engineer / Analytics to work for one of our clients, a major Technology Consulting firm with headquarters in Europe. They are experts in tailored technology consulting and services to banks, investment firms and other Financial vertical clients. Job location: San Francisco Bay area or NY City. Work Model: Ability to come into the office as requested Seniority: 10+ years of total experience About the role: The Data Engineer / Analytics Specialist will support analytics, product insights, and AI initiatives. You will build robust data pipelines, integrate data sources, and enhance the organization's analytical foundations. Responsibilities: Build and operate Snowflake-based analytics environments. Develop ETL/ELT pipelines (DBT, Airflow, etc.). Integrate APIs, external data sources, and streaming inputs. Perform query optimization, basic data modeling, and analytics support. Enable downstream GenAI and analytics use cases. Requirements: 10+ years of overall technology experience 3+ years hands-on AWS experience required Strong SQL and Snowflake experience. Hands-on pipeline engineering with DBT, Airflow, or similar. Experience with API integrations and modern data architectures.

We are seeking an experienced Data Engineer to design and optimize scalable data pipelines that drive our global data and analytics initiatives. In this role, you will leverage technologies such as Apache Spark, Airflow, and Python to build high performance data processing systems and ensure data quality, reliability, and lineage across Mastercard's data ecosystem. The ideal candidate combines strong technical expertise with hands-on experience in distributed data systems, workflow automation, and performance tuning to deliver impactful, data-driven solutions at enterprise scale. Responsibilities: Design and optimize Spark-based ETL pipelines for large-scale data processing. Build and manage Airflow DAGs for scheduling, orchestration, and checkpointing. Implement partitioning and shuffling strategies to improve Spark performance. Ensure data lineage, quality, and traceability across systems. Develop Python scripts for data transformation, aggregation, and validation. Execute and tune Spark jobs using spark-submit. Perform DataFrame joins and aggregations for analytical insights. Automate multi-step processes through shell scripting and variable management. Collaborate with data, DevOps, and analytics teams to deliver scalable data solutions. Qualifications: Bachelor's degree in Computer Science, Data Engineering, or related field (or equivalent experience). At least 7 years of experience in data engineering or big data development. Strong expertise in Apache Spark architecture, optimization, and job configuration. Proven experience with Airflow DAGs using authoring, scheduling, checkpointing, monitoring. Skilled in data shuffling, partitioning strategies, and performance tuning in distributed systems. Expertise in Python programming including data structures and algorithmic problem-solving. Hands-on with Spark DataFrames and PySpark transformations using joins, aggregations, filters. Proficient in shell scripting, including managing and passing variables between scripts. Experienced with spark submit for deployment and tuning. Solid understanding of ETL design, workflow automation, and distributed data systems. Excellent debugging and problem-solving skills in large-scale environments. Experience with AWS Glue, EMR, Databricks, or similar Spark platforms. Knowledge of data lineage and data quality frameworks like Apache Atlas. Familiarity with CI/CD pipelines, Docker/Kubernetes, and data governance tools.

We're the data team behind Midjourney's image generation models. We handle the dataset side: processing, filtering, scoring, captioning, and all the distributed compute that makes high-quality training data possible. What you'd be working on: Large-scale dataset processing and filtering pipelines Training classifiers for content moderation and quality assessment Models for data quality and aesthetic evaluation Data visualization tools for experimenting on dataset samples Testing/simulating distributed inference pipelines Monitoring dashboards for data quality and pipeline health Performance optimization and infrastructure scaling Occasionally jumping into inference optimization and other cross-team projects Our current stack: PySpark, Slurm, distributed batch processing across hybrid cloud setup. We're pragmatic about tools - if there's something better, we'll switch. We're looking for someone strong in either: Data engineering/ML pipelines at scale, or Cloud/infrastructure with distributed systems experience Don't need exact tech matches - comfort with adjacent technologies and willingness to learn matters more. We work with our own hardware plus GCP and other providers, so adaptability across different environments is valuable. Location: SF office a few times per week (we may make exceptions on location for truly exceptional candidates) The role offers variety, our team members often get pulled into different projects across the company, from dataset work to inference optimization. If you're interested in the intersection of large-scale data processing and cutting-edge generative AI, we'd love to hear from you.