Top Data Management Jobs in San Francisco Bay Area, CA

Leads applied AI and health data architecture initiatives for payer clients. Designs cloud-native platforms, pipelines, data models, integrations, and modernization roadmaps across AWS, Azure, Databricks, and Snowflake. Oversees data quality, governance, anonymization, performance optimization, and legacy migrations while advising stakeholders on AI-ready architectures. Manages multi-team delivery, resolves technical issues, presents solution tradeoffs, and coaches practitioners.

Leads and manages a business analyst team supporting a data platform. Partners with clients, product managers, engineers, and stakeholders to gather requirements, write user stories, perform data gap analyses and source-to-target mapping, validate data using SQL, and support data quality, lineage, QA, Agile delivery, change management, and documentation. Requires healthcare data expertise, people leadership, strong communication, and experience translating technical information for varied audiences.

Develops standardized business metrics, conducts customer and competitive analyses, forecasts key performance indicators, and delivers actionable insights to improve engagement, retention, churn, and overall company performance. Partners with Product, Marketing, Engineering, Data Science, and Data Engineering teams, supports strategic initiatives, implements analytical tools, improves intelligence-gathering processes, and presents findings to executive stakeholders.

Manages data operations for approximately 25 perinatal clinical research studies. Responsibilities include designing research databases, maintaining data dictionaries and documentation, extracting and cleaning complex datasets, developing dashboards and monitoring systems, conducting analyses, ensuring data quality and regulatory compliance, supporting reporting and publications, training research staff, and improving EHR, AI, and data-management workflows.

Lead the design, scalability, and governance of Turn/River’s BigQuery data platform and ETL/ELT pipelines. Integrate marketing and go-to-market systems, develop standardized data models and dbt packages, implement quality and CI/CD standards, and build AI-powered workflows using LLMs, RAG, and agent automation. The role also mentors engineers and analysts, defines the platform roadmap, and partners with Marketing, RevOps, Analytics, and Systems teams to deliver reliable, reusable data infrastructure.

Supports the development, migration, administration, and improvement of early care and education data systems. Manages data collection processes, system integrations, data quality, documentation, workflows, and requirements for third-party developers. Provides technical assistance and training to staff and partners, develops dashboards and reports, and conducts analysis to support program monitoring, compliance, continuous quality improvement, policy communications, and executive decision-making.

Manages research data for multisite neurodegenerative disease studies by collecting, integrating, cleaning, validating, documenting, and preparing datasets for research use, reporting, transfers, and system ingestion. Develops reproducible scripts, queries, and workflows; resolves discrepancies across internal and external sources; supports cohort feasibility analyses; and coordinates with investigators, laboratories, IT teams, and research collaborators.

The Manager, R&D Data Modeler designs and governs conceptual, logical, and physical data models for R&D analytics and digital initiatives. Responsibilities include source-to-target mapping, schema optimization, data integration, metadata and naming standards, FAIR and GxP governance, model versioning, architecture workshops, platform implementation consulting, and mentoring junior modelers. The role supports scalable, interoperable data ecosystems for analytics and AI workloads across data warehouses and cloud data lakes.

Owns the master data strategy, governance, quality standards, source-of-truth definitions, and integration architecture for an AI infrastructure supply chain. The role manages material, supplier, customer, asset, site, project, and financial master data across ERP, PLM, procurement, quality, inventory, finance, and project-control systems. Responsibilities include defining stewardship and approval workflows, monitoring data-quality KPIs, resolving cross-functional conflicts, supporting data migrations and integrations, and reporting master data health to leadership.

Owns go-to-market data governance, quality, enrichment, and automation across Salesforce and connected systems. Designs standards, controls, source-of-truth models, AI and LLM-powered workflows, monitoring, and remediation processes. Partners with Business Operations, Marketing Operations, Customer Success, Finance, IT, and security to improve data accuracy, compliance, reporting, and operational efficiency. Leads data management and automation projects from planning through measurable outcomes.

Coordinates Oracle EPM Cloud data governance, metadata management, integrity reviews, reconciliations, restatements, reporting controls, audit support, and system enhancements. Partners with Finance, Accounting, IT, and business stakeholders to resolve data variances, document requirements, support SOX compliance, maintain reporting outputs, and improve enterprise financial reporting processes.

Manages biospecimen and genetics research data workflows, including data ingestion, validation, quality control, querying, integration, reporting, and dataset preparation. Develops reproducible scripts and maintains data systems supporting specimen inventories, billing, research requests, and transfers. Investigates discrepancies, documents data processes, supports cohort feasibility, and collaborates with biospecimen, laboratory, clinical, and research stakeholders.

Designs, develops, and maintains research and operational databases, data models, ETL workflows, dashboards, data collection tools, and research websites. Administers secure applications and storage servers, manages permissions and data access under VA security requirements, performs data quality assurance, installs statistical applications, and supports and trains end users. The role also coordinates data governance, documentation, integration, modeling, and analysis across UCSF, VA, and research partners.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages sprint planning, releases, DevOps metrics, project delivery, and stakeholder communication. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, and ensures reliable, well-documented data solutions that support organizational reporting and analytics needs.

Leads the enterprise business intelligence and analytics strategy, manages BI and data analysts, and oversees analytics roadmaps, projects, platforms, dashboards, and vendor relationships. Translates complex data into actionable financial and business insights, supports SAP and Power BI platform upgrades, manages delivery timelines and resources, and coaches team members. The role also drives stakeholder engagement, continuous improvement, talent development, and data-driven decision-making across the organization.

Owns analytical projects across customer onboarding, transaction behavior, and platform operations. Designs dashboards, KPI frameworks, reporting workflows, and data pipelines while delivering actionable insights. Partners with Engineering, Product, Payment Operations, Compliance, and Finance; mentors colleagues; manages project scope and priorities; resolves ambiguity; and drives cross-functional alignment. Requires advanced SQL and data modeling skills, BI tool experience, analytical scripting familiarity, FinTech domain knowledge, and 4+ years of analytics experience.

Leads quantitative data analytics and insights for investment risk forecasting, portfolio construction, performance measurement, and reporting. Designs Python- and R-based analytical tools, production data platforms, quality frameworks, and quantitative models. Supports portfolio managers and analysts through risk analysis, statistical modeling, data integrity controls, reporting automation, and infrastructure improvements. Works with multi-asset security data, factor models, derivatives, valuation, VaR, stress testing, and performance attribution while providing technical guidance and solutions to business partners and senior leadership.

Leads the enterprise data platform engineering team and owns its reliability, security, scalability, performance, cost, and operational support. Oversees relational, NoSQL, and analytical platforms; data movement and transformation; infrastructure as code; GitOps; database automation; high availability and disaster recovery; monitoring; compliance; and incident response. Manages engineers, on-call operations, technology selection, vendor evaluations, budgeting, and cross-functional delivery while remaining hands-on in architecture, administration, performance tuning, and complex technical escalations.

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.

Owns Kestra’s enterprise data platform, Microsoft Fabric transition, data architecture, governance standards, and Master Data Management program. Leads platform reliability, semantic models, self-service BI enablement, data protection, compliance, vendor resources, and technical teams. Advises executives and functional data owners while preparing governed data assets for AI, automation, and enterprise reporting.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, campaign reporting, experimentation, and lifecycle analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and generate actionable insights. Builds churn, retention, segmentation, and LTV models while evaluating paid acquisition and customer growth opportunities using SQL, Python, BI tools, marketing platforms, and modern AI-enabled workflows.

Leads the design, delivery, and optimization of data architecture, pipelines, semantic layers, data quality frameworks, and Tableau BI platforms. Partners with stakeholders to manage requirements, project plans, sprint execution, monitoring, governance, and release delivery. Guides data engineers, manages team members, and supports AI-enabled analytics capabilities. Requires strong SQL, Spark, PySpark, SSIS, cloud data platform, CI/CD, Tableau infrastructure, and data engineering expertise.

Leads and grows the data engineering team while owning enterprise data architecture, ingestion, modeling, semantic layers, governance, secure APIs, and reliable platform outcomes. Partners with finance, operations, sales, marketing, and product to deliver governed, traceable data for products and AI agents. Sets the roadmap, manages vendors and costs, reviews architecture, contributes code, and delivers cross-functional data platform projects.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Manage and grow Ashby’s data analytics team while remaining hands-on with analytical work. The role includes hiring and coaching analysts, improving data practices, prioritizing requests, developing specialization, maintaining analytical quality, and guiding responsible AI adoption. The manager will work across raw and modeled data, dbt models, metric definitions, dashboards, and internal data products, partnering closely with business teams in a remote, high-growth B2B SaaS environment.