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

Lead and grow a data engineering team, define data platform architecture and reliability (warehouse, ingestion, transformations, orchestration), set engineering standards, partner cross-functionally to support analytics and ML, and translate technical decisions for executive stakeholders.

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

Lead development and implementation of data governance strategies, manage client accounts and deliverables, supervise and mentor teams, analyze data to inform business decisions, ensure compliance with firm standards, and identify improvements in data management processes.

Lead development and execution of data-driven strategies, manage project planning and delivery, mentor and coach team members, ensure quality and ethical standards, design cloud data architectures and governance frameworks, analyze complex datasets to produce actionable insights, and drive continuous improvement using advanced analytics and modern data technologies.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Oversee data ingestion and transformation, manage finance data sources, ensure scalability and reliability of data platforms, and collaborate with stakeholders to align data engineering with organizational goals.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Lead and grow a data engineering team to build and maintain data models, batch and streaming ETL pipelines, and governance practices. Partner with platform, product, and business stakeholders to ensure reliable, governed, and scalable data for BI, ML, and operations while driving AI strategy and technical direction.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

Manage operations for client engagements in the life sciences sector, leading projects, enhancing data processes, and developing data-driven solutions while maintaining client relationships.

The Manager, Data Management leads a firm-wide data management program, ensuring data quality and accountability across master data domains while partnering with stakeholders and managing staff.

Lead a team of data scientists to optimize Databricks infrastructure using data science for capacity planning, reliability, and performance improvements.

The Manager, Data & Analytics will generate insights, analyze data, collaborate across functions, and improve analytics practices, primarily in the banking sector.

Lead the Data & ML Platform team, overseeing data engineering, real-time systems, and ML enablement, while building reliable, scalable data platforms and improving data architecture.

Lead and scale the enterprise data hub: manage the data warehouse, build reliable ingestion and transformation pipelines, implement governance and data modeling best practices, deliver GTM-ready metrics, enable AI/ML use cases, prioritize the data roadmap, and lead/mentor a data engineering team.

Lead a hands-on analytics team (player-coach) delivering deep-dive analyses, A/B testing, KPI dashboards, metric governance, and stakeholder-facing insights while managing and developing analysts and partnering with data engineering.

Lead combined Analytics Engineering and Data Engineering teams owning the end-to-end data roadmap from ingestion to trusted metrics. Manage hiring and performance, set architecture and platform investments, enforce testing/monitoring/documentation standards, review technical decisions, and partner with product and business stakeholders to translate needs into prioritized deliverables.

Lead and grow a data engineering team to design, build, and operate scalable, cost-efficient data lakehouse pipelines and infrastructure. Define technical strategy, enforce engineering best practices (CI/CD, testing, monitoring), own roadmap and capacity planning, collaborate with product and engineering stakeholders, manage on-call and operational automation, and hire talent. Ensure data governance, lineage, and compliance in a regulated healthcare environment.

Administer and maintain CRM and data systems (EveryAction, Salesforce, BigQuery, CARTO), respond to staff and affiliate data/tech requests, ensure data quality and de-duplication, create and deliver training materials, build network capacity, manage vendor/consultant relationships, and travel occasionally for in-person trainings and support.

Lead analytics and reporting for DTC channels, develop dashboards and quality metrics, ensure data integrity, support vendor oversight, and drive performance improvements using data tools and quality processes focused on Medicare sales.

Lead enterprise data governance, quality, MDM, cleansing, validation, reconciliation, and migration efforts to support ERP implementations and large transformation programs. Develop governance frameworks, coordinate data readiness and testing, produce executive reporting and presentations, manage project plans and governance artifacts, and drive stakeholder alignment and change management.

Lead a small data engineering team partnering with Engagement Marketing to build scalable ETL/ELT pipelines, ensure data quality, define marketing metrics, integrate trusted data with ad platforms, and drive the data roadmap to enable measurement and growth.

Lead a team delivering actuarial statistical reporting, regulatory submissions, and analytics. Ensure compliance with bureau requirements, drive automated reporting and UAT for policy/claims systems (Guidewire, Solartis), partner with actuarial, technology, and vendors, and manage stakeholder/regulator interactions while mentoring staff and improving data governance and reporting processes.