Top Data Engineer Jobs in San Francisco Bay Area, CA

Build and operate scalable data pipelines and platforms that transform LiDAR, imagery, and GPS sensor data into reliable geospatial deliverables. Responsibilities include data ingestion, AI processing, quality monitoring, workflow automation, and AWS cost and performance optimization. The role partners with Data Operations, GIS, product, AI, and analytics teams and requires end-to-end ownership, including on-call support.

Design, build, and optimize scalable data pipelines, warehouses, lakes, and processing systems for analytics and business intelligence. Ensure data quality, security, reliability, and cost efficiency while collaborating with data scientists, analysts, and engineers. Maintain technical documentation, support Databricks solutions, and apply emerging data engineering technologies. The role requires strong programming, SQL, ETL, orchestration, data modeling, cloud, and big data expertise.

Own the design, development, maintenance, and performance of Microsoft Fabric data pipelines, centralized semantic models, DAX measures, and data architectures supporting marketing analytics. Ensure data quality, documentation, consistency, and trustworthy business metrics. Partner with data engineering and business intelligence teams on infrastructure and deployments, investigate model issues, perform complex analysis, mentor apprentices, and contribute to applied AI initiatives.

Architects and leads enterprise data engineering platforms, including scalable ETL/ELT pipelines, streaming frameworks, cloud infrastructure, data warehouses, and AI/ML data systems. Establishes engineering standards, governance, monitoring, data quality, security, and schema management practices. Designs AI agents using LLMs, partners across business and infrastructure teams, and makes high-impact architectural decisions for reliable, secure, multi-region data environments.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Lead the technical direction and production operations of Handshake’s data platform, including Airflow orchestration, ingestion, batch and streaming pipelines, BigQuery, Kubernetes, and cloud infrastructure. Build reliable workflow development and deployment paths, improve data resilience and observability, establish data contracts, support AI workloads, troubleshoot incidents, and mentor engineers across application, analytics, ML, and cloud teams.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Designs, builds, tests, and maintains scalable data infrastructure and ETL/ELT pipelines for mission partners. Uses AWS Glue, Glue Catalog, and Step Functions to integrate, migrate, transform, and validate data. Develops data models, schemas, queries, and production-ready datasets while collaborating with analytics and business teams. The role may also support AI/ML pipelines, data governance, DevOps workflows, Databricks, and real-time streaming architectures.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Build and scale data products supporting analytics, engineering services, and AI/ML models. Responsibilities include optimizing queries and data pipelines, developing data models and governance processes, administering ETL/ELT and reverse ETL orchestration, and creating documentation and training. The role also involves mentoring, interviewing, and hiring data engineers while helping define the organization’s data engineering strategy.

Lead Notion’s Data Foundations team by setting multi-quarter platform strategy, developing engineers and technical leaders, and guiding complex initiatives across data lakes, streaming, distributed compute, governance, and reliability. Drive enterprise-ready capabilities including data residency, access controls, lifecycle management, and key management. Improve Kafka, Debezium, S3/Iceberg, Spark, EMR, and Athena scalability, reliability, usability, and cost efficiency while partnering across engineering, AI, search, infrastructure, and security teams.

Build and operate batch and streaming data pipelines on Google Cloud for analytics and machine learning. Ingest, transform, validate, and curate datasets in BigQuery; implement data quality, governance, security, monitoring, Terraform infrastructure, and CI/CD. Provide technical direction, lead code reviews, and document data processes. The role requires cloud data engineering experience, technical team leadership, strong SQL and Python skills, and eligibility for a federal Public Trust investigation.

Lead pre-sales technical engagements to design and optimize scalable cloud lakehouse data platforms (Snowflake/Databricks). Provide architecture reviews, ETL/ELT pipeline design, governance and AI-ready data foundations, support services sales, and translate technical tradeoffs into actionable recommendations for engineering and executive stakeholders.

Build and maintain scalable data pipelines, infrastructure, data models, and APIs using Python, SQL, Airflow, Snowflake, dbt, Terraform, and cloud services. Ensure data quality, security, observability, and reliability while collaborating with analysts, product managers, and engineering teams. Own aspects of the FastAPI data service, including design, deployment, monitoring, and performance. Handle sensitive healthcare data in compliance with governance standards.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Build and operate production-grade ELT pipelines from Benchling, Salesforce, and third-party systems into Snowflake using dbt. Own ingestion, transformation, warehouse, BI, governance, monitoring, testing, access controls, privacy, cost, and performance. Partner with AI engineering to provide trusted data for internal AI applications and agentic workflows, contribute to warehouse and metrics-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build and own marketing data, attribution, tracking, audience sync, and conversion infrastructure. Integrate product, CRM, warehouse, and advertising platforms; send offline conversions and LTV signals to ad platforms; maintain event schemas and dashboards; debug data discrepancies; implement privacy-safe tracking; and partner cross-functionally with growth, product, marketing, and data teams.

Own and scale Collective’s data platform by building reliable batch and event-driven pipelines into BigQuery, modeling data with dbt, and enforcing quality, observability, security, and cost standards. Partner with engineers, analysts, and business stakeholders to deliver trustworthy datasets, metrics, reporting, and AI-enabled warehouse access. The role includes production ownership, incident response, governance, infrastructure practices, and support for analytics and LLM-based tools.

Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Build, test, document, and optimize data pipelines and foundational datasets for Figma’s data warehouse. Interns will support data quality, scalability, analytics, experimentation, and cross-functional data needs while owning a scoped project from definition through implementation, testing, documentation, and results sharing.