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

Lead the architecture and development of Vercel’s Finance Data Platform supporting revenue recognition, consumption-based billing, forecasting, reporting, and compliance. Build reliable batch and real-time pipelines using Kafka, ClickHouse, Tinybird, and Snowflake; establish governance, lineage, auditability, monitoring, and fault tolerance; translate Finance and Accounting requirements into scalable systems; guide architecture and roadmap decisions; collaborate with Security, Legal, Compliance, Audit, FP&A, and RevOps; and mentor data engineers.

Leads a data engineering team responsible for data modeling, batch and streaming ETL pipelines, analytics-ready datasets, data governance, and data quality. Hires, coaches, and manages engineers while setting technical direction and partnering with engineering, product, and business stakeholders. Oversees scalable data foundations across regulatory, customer, business, content, and growth domains, and advances practical AI automation and AI-ready infrastructure.

Own the analytics foundation for Fieldguide Insights: design the semantic layer, define canonical tables/metrics, migrate report logic into governed dbt models, harden pipelines with monitoring and alerts, ensure data quality, and partner across product, engineering, and customers to enable reliable BI and emerging LLM/MCP analytics.

The Staff Data Analyst will lead data strategy for Compliance, analyzing regulatory engagements, and driving effectiveness through actionable insights and collaboration across various teams.

Client-facing solutions architect who designs and delivers scalable cloud, data, and AI-enabled solutions. Leads end-to-end projects, mentors teams, ensures scalable/resilient architectures, modernizes data ecosystems, drives delivery methodology, and develops business cases while collaborating globally with clients and ZS teams.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Build and maintain ELT pipelines, data models, semantic layers, and personalization signals for client retention and marketing tests. Partner with machine learning, product, marketing, design, and architecture teams to prepare predictive data, connect signals to CRM channels, support real-time event processing, and document a scalable data foundation. Use AI coding assistants daily to accelerate development, testing, and reviews in a fast-moving discovery environment.

Design and build scalable data pipelines, models, and dashboards for supply chain KPIs. Partner with business stakeholders to enable forecasting, scenario planning, data governance, automation, and actionable reporting for leadership and projects.

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.

Design, build, and deliver scalable data pipelines and products end-to-end. Partner with analytics, product, and engineering to ingest diverse data sources, implement dbt transformations, ensure data quality, CI/CD, observability, monitoring, and cost/performance optimization. Leverage AI/automation for self-service tooling and intelligent pipelines while driving stakeholder alignment and reliable production operations.