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

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Build scalable, secure GCP data pipelines and reusable frameworks supporting machine learning and AI workloads. Develop Python, Go, SQL, and Beam transformation libraries; implement orchestration, data modeling, feature stores, semantic layers, governance, quality, lineage, and observability. Optimize workload performance and costs, migrate models to GCP, and partner with data scientists. The role also includes documentation, workshops, CI/CD integration, and occasional off-hours production support.

Analyzes large datasets to identify insights supporting mergers, acquisitions, diligence, and value creation decisions. Develops data models, dashboards, visualizations, and statistical or predictive analyses; cleanses and validates data; uses Python, SQL, Alteryx, Power BI, or Tableau; researches client and market information; documents findings; and collaborates with senior team members on project deliverables.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

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.

The Data Scientist II will lead and execute health economic outcomes research projects, collaborate with stakeholders, and build analytical infrastructure while adhering to regulations.

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.

Leads and grows a data platform engineering team responsible for high-volume financial data ingestion, Databricks lakehouse architecture, canonical financial models, orchestration, streaming, reliability, and infrastructure. Drives migrations from legacy pipelines, establishes data quality and platform standards, optimizes performance and costs, and partners with product, accounting, ML, and infrastructure teams to deliver scalable, auditable financial systems.

Build and maintain scalable dbt data models, transforming raw data into trusted, documented datasets. Own warehouse data modeling and metric definitions, establish testing and code review standards, monitor data quality, optimize query performance, and troubleshoot reporting discrepancies. Partner with engineers, analysts, and business stakeholders to translate business needs into reliable pipelines. Mentor junior engineers and contribute to technical best practices and tooling decisions.

Own measurement strategy and performance improvement for Snap’s advertising measurement solutions. Lead experimental design, causal analytics, benchmarking, meta-analyses, governance, and scalable measurement initiatives. Advise executives and external partners, influence product roadmaps, develop thought leadership, and build analytical fluency across teams. Apply advanced statistics, SQL, AI tools, dashboarding, and data visualization to guide advertising efficacy decisions and identify innovation opportunities.