Top Senior Data Engineer Jobs in San Francisco Bay Area, CA

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Design and operate scalable data systems for People Analytics, ensuring reliability and privacy of sensitive employee data. Collaborate with leadership to create data products for decision-making.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

The Data Engineer will architect and develop robust data frameworks for data platforms, focusing on data transformations and automated workflows. Responsibilities include collaborating with stakeholders, ensuring data quality, and maintaining high engineering standards.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

Design, build, and maintain a high-quality data platform delivering private market data to internal and external clients. Implement features, maintain functionality, perform code reviews, unit tests, CI/CD deployments, and collaborate with Product, Design, and Engineering to deliver requirements on schedule.

Design and implement scalable data models and ETL pipelines (batch and streaming), ensure data quality/SSOT, collaborate with product and platform teams to resolve issues, contribute to data governance, and explore practical integrations between data engineering and AI.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Design, build, and operate scalable cloud data pipelines and platforms (development through production). Embed security, automation, CI/CD and testing; monitor, tune, and resolve incidents; provide technical design, mentoring, and cross-functional collaboration to deliver reliable healthcare-focused data products.

Lead design and delivery of scalable, secure cloud data pipelines and data products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0, dimensional), embed data quality/security/observability, optimize performance and cost, drive CI/CD/IaC/DataOps practices, mentor engineers, and support AI/ML-ready datasets within an agile DevSecOps pod.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

As a Senior Data Engineer, you will automate processes, optimize data handling, build data products, and govern data while mentoring junior engineers.

The Scientific Data Engineer will design data architecture, build ETL/ELT pipelines, implement data quality checks, and develop semantic layers for AI-consumable data products in healthcare research.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Design, implement, and maintain scalable data infrastructure and real-time processing for analytics platforms. Migrate systems from AWS to on-prem Kafka/Spark, implement data transformations, support production, optimize pipelines, query and analyze data, and build scripts and APIs to operationalize data science work.

Own and build the end-to-end data layer: extraction pipelines from messy supplier emails and PDFs, the unified data model, reliable observable pipelines, and ML-driven extraction evaluation. Drive extraction accuracy, data quality, lineage, monitoring, and automation to scale coverage and enable engineering and product to depend on the data layer.

Own and scale ReadyOn's data platform: design/build real-time and batch pipelines, implement ingestion and transformation workflows, model schemas in Snowflake and PostgreSQL, ensure observability and data quality, build connectors and feature-store roadmap, and provide technical leadership and mentorship across data, AI, and backend teams.

Develop and maintain data infrastructure for safety data analysis, build scalable data pipelines, conduct exploratory analyses, and support regulatory reporting in AV safety.

As an Analytics Engineer, you will create scalable solutions for data reporting, build data pipelines, and establish high data integrity standards while collaborating with multiple teams.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Design, build, and optimize enterprise-scale data models, pipelines, and cloud-native architectures to support analytics, AI/ML, and reporting. Lead data governance, metadata management, lineage, access controls, and data quality. Partner with stakeholders to translate business needs into scalable, secure, and high-performance data infrastructure, and evaluate emerging technologies to evolve the data platform.