Top Data Engineer Jobs in San Francisco Bay Area, CA

The Data Migration Engineer migrates customer data into SaaS products, ensuring data quality and business continuity while collaborating with teams and managing customer requirements.

Design and implement end-to-end data integration and warehousing solutions for large volumes. Implement Hadoop/Hive data movement, script in Linux shell and Python, develop SQL for integration and analysis, and perform high-volume DW performance tuning. Vertica experience is a plus. Requires technical independence and a BS in CS/EE.

Lead design, build, and maintain scalable data pipelines and infrastructure using Azure Databricks, ADF, and SQL Server. Develop OLTP and dimensional models, integrate Salesforce and SaaS sources, implement batch and real-time processing, enforce data governance and CI/CD, troubleshoot performance, mentor team members, and drive improvements to reduce technical debt and improve cost and performance.

Lead architecture and evolution of Headway's data platform (warehouse, ingestion, orchestration, CI/CD, monitoring, cloud infra). Serve as technical anchor across analytics, product, and ML teams, drive platform roadmaps, set standards, mentor engineers, and own end-to-end infrastructure decisions for scale and performance.

Build and maintain data infrastructure to measure agent behavior and user interactions, design evaluation frameworks for AI agents, analyze user behavior to inform product strategy, and partner with engineering and leadership to solve high-impact business problems using data.

As a Senior Data Engineer, you will design and maintain scalable data models, build production data pipelines, and support analytics across various business functions.

As an Analytics Engineer, you'll manage the data stack, design database architecture, build data pipelines, and oversee reporting and data quality.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Build and maintain scalable data pipelines, trusted datasets, and reporting systems to support infrastructure deployment, hardware operations, capacity planning, and supply chain analytics. Partner with stakeholders, improve data quality, lineage, and observability, and enable executive reporting, forecasting, and operational reviews. Integrate new telemetry sources and document systems for long-term maintainability.

The Data Engineer I will build and maintain data pipelines, contribute to platform code, and integrate data sources while growing alongside senior engineers in a production-focused role.

Support a Department of State data engineering program: review data policies and governance, define requirements, build and maintain Palantir-based data pipelines, clean and integrate datasets, detect and remediate data fidelity and pipeline issues, produce analyses and visualizations, and coordinate with non-technical users and stakeholders.

Build and own customer data ingestion and large-scale pipelines powering AI workers. Design syncing, retrieval, and data lake systems, improve reliability/observability, partner with product/backend teams, and contribute across infrastructure to give AI agents access to enriched customer data.

As a Data Engineer at Calico, you'll develop infrastructure for research data, collaborate on projects, mentor junior engineers, and ensure data governance.

Responsible for designing, developing, and maintaining databases and ETL systems to support business applications and business intelligence. Collaborates with stakeholders to align business requirements and leads the design of automated solutions for operational efficiency.

The Principal Data Engineer will manage the analytics data architecture, engage cross-functionally, and create reliable data systems using Snowflake and Python on AWS.

Design and maintain pipelines and tooling to ingest, validate, model, and serve multi-modal driving data for ML training, evaluation, and debugging. Collaborate with ML, vehicle software, curation, and fleet teams to scale the data stack and make datasets discoverable and queryable.

As a Data Analytics Engineer, you'll build and manage data models in Snowflake, structure datasets for optimal AI performance, and own data transformation while ensuring quality and accessibility for all teams.

Lead design and hands-on delivery of a next-generation data platform and pipelines (batch, ELT, near-real-time) powering BI, experimentation, and ML. Own platform architecture, data modeling, governance, observability, and mentor engineers while modernizing legacy systems and driving AI-first data engineering practices across cross-functional teams.

Build and support high-performance data solutions, design and operate ETL pipelines and data warehouses, monitor production analytics tools, troubleshoot system issues, collaborate with product teams, and support TechOps for analytics infrastructure.

Design, build, and maintain scalable batch and real-time data pipelines and platform components. Produce curated datasets, ensure data quality, observability, and lineage, and collaborate cross-functionally to deliver data products that support analytics, experimentation, and ML.

Build and maintain ingestion pipelines, Python services, and dbt models on AWS and Snowflake. Own operational on-call responsibilities, migrate pipelines to Airflow, contribute to CI/CD, collaborate with BI/Analytics, and mentor junior teammates.

Lead end-to-end data engineering on GCP: design scalable data platforms and architectures (lake/lakehouse/warehouse), build and optimize BigQuery/Spark pipelines, ensure data quality/security, enable ML workflows with Data Science, mentor engineers, and own delivery and performance.

Design, build, and optimize data pipelines and storage solutions. Ensure data accuracy and performance, provide technical support, and collaborate with data scientists and stakeholders to resolve data issues and meet requirements.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Design, build, and maintain scalable ETL/ELT data pipelines and BigQuery data models. Implement Airflow workflows, CI/CD for deployments, ensure data quality and performance, and collaborate with data scientists, analysts, and business teams to deliver reliable analytics-ready data.