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

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.

The role involves assessing fraud program effectiveness, compliance with regulations, and providing strategic insights for risk management within the financial services industry.

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.

Design and build data warehouse architecture, develop ETL/ELT pipelines, implement data governance, and partner with teams to improve data systems.

The Cloud FinOps Practitioner will design and implement cloud financial operations, focusing on cost optimization, governance, and collaboration across teams to enhance efficient cloud usage.

As a Staff Data Engineer, lead the technical roadmap for data infrastructure, manage complex data architectures, and mentor engineers while driving the organization's data platform innovations.

Lead fraud detection efforts by analyzing data, developing detection methods, collaborating with teams, optimizing fraud rules, and monitoring performance metrics.

The Senior Analytics Engineer will enhance data models, optimize performance in Snowflake, and bridge data engineering with analytics teams for strategic decision-making.

The Data Scientist will build analytics infrastructure, conduct analysis and modeling, and collaborate with teams to enhance data impact and insights. Responsibilities include exploratory analysis, model building, and maintaining datasets for student success and operational efficiency.

Analyze auction data to identify performance drivers and risks, collaborate with commercial teams, and provide actionable insights to optimize DSP outcomes.