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

Lead product-focused analytics for customer-facing interfaces: define and maintain interface metrics, measure design system adoption, run large-scale A/B tests, optimize localization, partner with Product/Design/Engineering, and enable data-driven decision making across teams.

Designs and maintains scalable data infrastructure, ETL/ELT pipelines, canonical datasets, and AI context layers. The role improves data quality and alignment across sales and finance systems, investigates data inconsistencies, leads requirements through UAT and implementation, supports analytics self-service, and partners with cross-functional teams to deliver process improvements and reliable business data.

Lead the strategy and governance for GTM analytics across pre-sales, post-sales, solutions engineering, forward-deployed engineering, and professional services. Establish trusted source-of-truth metrics, data models, semantic layers, self-service analytics, and AI-assisted querying. Partner with Data, Finance, Sales, and Services leaders to resolve metric definitions, improve data access, consolidate reporting, and guide cross-functional programs without direct authority.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Leads customer intelligence analytics for the Customer Experience organization by designing measurement frameworks, conducting ambiguous investigations, building dashboards and reusable Snowflake-backed data products, and partnering with data scientists on health scoring, forecasting, segmentation, and validation. Communicates actionable findings to senior stakeholders and operationalizes reporting solutions for scalable handoff.

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 data analysis and modeling for Growth and Monetization, focusing on sales trials, product-led sales, pricing, packaging, and monetization. Partner cross-functionally to generate actionable insights, maintain dbt models and ELT pipelines, improve data workflows, evaluate emerging tools, and enable colleagues to use Hex effectively. The role requires strong analytical judgment, SQL and dbt expertise, and an interest in applying AI to data work.

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 statistical measurement and client intelligence initiatives using healthcare, prescription, and claims data. Develop performance benchmarks, evaluate program impact, apply statistical and machine learning methods, advise data scientists, support survey-based measurement, manage analytics vendors, mentor analysts, and present findings to marketing teams and clients. The role requires advanced SQL and Python, healthcare data expertise, vendor management, and extensive quantitative experience.