Top Data Engineer Jobs in San Francisco Bay Area, CA

Design, build, and maintain data pipelines and tooling; collaborate with cross-functional stakeholders to deliver governed, high-quality datasets; own data quality and drive adoption of produced datasets.

The Data Engineer will build datasets and pipelines for marketing and sales reporting, collaborating with various teams to support data needs and ensure scalable solutions.

As a Data Engineer, you will design and maintain data pipelines, build data storage systems, and support analytics workflows, ensuring data reliability and performance. You will collaborate with engineers to define data structures and improve data management practices.

As a Senior Data Engineer, you will design and maintain data pipelines, define governance standards, and collaborate across teams to enhance Headway’s data architecture.

Transform Electronic Health Records (EHR) data into structured assets, build scalable data pipelines, and apply Generative AI techniques for analytics within a healthcare-focused team.

As a Staff Data Engineer, you'll architect and model data, create ETL pipelines, and standardize datasets for compliance teams. You'll guide best practices and lead data solutions while collaborating with various stakeholders.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

The Data Engineer will design and manage data pipelines, develop datasets for product metrics, and collaborate with teams to ensure robust data integration and processing.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

As Lead Data Engineer, you will guide a team, manage the data platform, and oversee the development of ML infrastructure and feature stores.

Build and maintain cloud data pipelines and ML feature/model workflows in Snowflake and AWS. Implement data ingestion, transformations, quality checks, monitoring, and documentation. Collaborate with data scientists, analysts, and product teams to support analytics, audience insights, rapid prototyping, and reliable production deployments.

As a Data Engineer, you will build and own the data infrastructure, design pipelines, ensure data quality, and implement DataOps practices while collaborating closely with product and engineering teams.

Design, build, and maintain ELT pipelines and analytics-ready data models to support reporting, KPIs, and downstream ML use cases; implement data quality, optimize pipeline performance, document lineage, and ensure governance and privacy.

Design and maintain ETL pipelines and graph data models to ingest security telemetry into an AWS Neptune knowledge graph, enable ML-driven threat detection, optimize query performance, ensure data quality and compliance, and document workflows.

The Senior Quantitative Data Engineer will build and maintain scalable data pipelines for quantitative investment research, ensuring data integrity and compliance, while collaborating with various teams for data architecture and product strategy.

Build and maintain integrations and ETL/ELT pipelines connecting K-12 Student Information Systems and external systems. Design scalable data workflows, monitor and troubleshoot data flows, perform QA and reconciliation, document data models, and collaborate with stakeholders on enrollment, attendance, and performance data needs.

This role involves building and maintaining data pipelines, developing data models, and ensuring data discoverability while enabling scalable analytics on blockchain data.

The Data Engineer will develop and optimize data pipelines, manage data architecture, support cross-functional teams, and ensure data security and integrity.

The Data Engineer at Netflix builds systems for efficient data processing and modeling, collaborating with various teams to empower analytical insights and product features.

Design, build, and maintain scalable, reliable data pipelines and services. Write well-tested Python and SQL code, work with orchestration (Airflow), containers, and cloud infrastructure. Collaborate cross-functionally to deliver data products and improve development processes.

Build and maintain Databricks-based ETL/ELT pipelines, transform and model data, optimize workflows, monitor and troubleshoot pipelines, and collaborate with cross-functional teams.

The Data Engineer will build ETL pipelines and data infrastructure, ensure data quality, and implement modern data warehousing for analytics improvements, while collaborating with stakeholders across teams.

The Data Engineer will manage data ingestion in Salesforce, standardize lead data, ensure data quality, and optimize integration workflows.

The Data Engineer will build scalable privacy-centric data frameworks, engineer data pipelines, and ensure compliance with privacy regulations at Netflix.

As a Data Engineer, design and maintain data pipelines, ensure data quality, and support AI workflows using modern tools and cloud environments.