Top Senior Data & Analytics Jobs in San Francisco Bay Area, CA

The Senior Analytics Engineer will manage the analytics layer, owning dbt projects, orchestrating data pipelines, defining metrics, and ensuring data quality while mentoring the data team.

Own and build canonical data models and metric definitions using dbt and cloud warehouses. Create tests, documentation, lineage, and data quality checks; reconcile metrics across teams and payer customers; partner with analysts, data scientists, product, engineering, and finance; optimize warehouse performance, cost, and PHI-aware access patterns.

Build the canonical customer operating data layer by integrating Salesforce, Jira, Langfuse, Snowflake, product telemetry and other sources. Surface health and adoption signals, build executive reports and visualizations, implement reconciliation, freshness and backfill logic, support automation and AI summaries, and partner with business and BI stakeholders to translate lifecycle definitions into durable, production-ready workflows and data structures.

The Quantitative Methodologist Expert will design and deliver complex quantitative research solutions, applying advanced methodologies to address business questions and drive data-driven outcomes.

The Director of Biostatistics leads a team in clinical research, developing statistical plans, collaborating across departments, and ensuring regulatory compliance.

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

The Senior Analyst in AML KYC will conduct detailed customer onboarding, monitor client relationships for compliance, and update KYC records while utilizing AI tools for efficiency.

The Senior Manager of Data Engineering will build a data platform, lead a team, and ensure data accessibility to support AI and business operations.

The Data Scientist in Finance will analyze financial data, create dashboards, collaborate on data engineering, and find improvements for the business.

Build and maintain analytical data models, dashboards, KPIs, and self-serve analytics for Revenue. Partner with Data Engineering and cross-functional teams to enable GTM analytics, forecasting, attribution, data quality, and governance while scaling the analytics function.