Top Data & Analytics Jobs in San Francisco, CA

Develops resource allocation, demand forecasting, and capacity models for Customer Success. Analyzes quantitative and qualitative data, identifies trends and risks, and translates findings into strategic recommendations. Partners with finance, planning, delivery, and leadership stakeholders to validate assumptions, resolve complex business problems, and support regional model adoption. Provides senior leadership with actionable insights while managing cross-functional priorities and ambiguity.

Designs and builds scalable ETL infrastructure, trusted core datasets, data pipelines, automated workflows, and self-service data tools. Partners with data scientists and business stakeholders to define requirements, monitor pipelines, establish delivery SLAs, reduce operational costs, and support product and business decision-making. The role requires strong SQL, data modeling, cloud data platform, object-oriented programming, and AI-enabled data development expertise.

Develop causal economic analyses and econometric models using Mastercard and public datasets. Translate business questions into identification strategies, lead projects from data ingestion through validation and communication, apply causal machine learning and experimentation methods, prototype scalable analytics, guide scientific best practices, and present findings to clients and nontechnical stakeholders.

Design and build Databricks ETL pipelines and Gold Layer tables, develop semantic models and documentation for AI-enabled BI, create dashboards and ad-hoc analyses, translate marketing and sales requirements into analytics-ready datasets, and partner with Data Engineering and stakeholders to enable self-serve reporting and agentic AI workflows.

Build and operate scalable batch and streaming data platform infrastructure, including ETL/ELT frameworks, data quality, governance, lineage, schema management, access controls, and observability. Own the technical roadmap, architecture, reliability, and incident response for core platform systems. Partner with engineering, analytics, data science, and marketing teams on data contracts and domain onboarding. Mentor engineers and participate in an on-call rotation while ensuring compliance with fintech requirements.

Analyze complex data, develop business intelligence tools, dashboards, and metrics, and build data solutions for self-service analytics. Manage data quality, metadata, lineage, governance, security, and access controls. Partner with business and technology teams to deliver insights and improve data management. The role uses Python, R, Spark, SQL, Snowflake, AWS, and visualization tools.

Twelve-week Data Engineer internship supporting Coinbase’s Platform product group. Responsibilities include building and maintaining data pipelines, developing scalable cloud-based infrastructure, integrating internal and external data sources, supporting analytical deep dives, documenting findings, and applying data governance, security, and performance optimization practices. The role uses Python, SQL, cloud platforms, and AI/API technologies while collaborating with senior engineers and cross-functional partners.

The Senior Data Engineer will design, implement, and maintain data infrastructure and ETL pipelines, optimize data flow, and develop reporting solutions. They will collaborate with teams to enhance data quality and support data-driven decision-making.

Define enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, OLTP, OLAP, AI, and vector-store use cases. Establish standards for data quality, governance, lineage, access, observability, SLAs, and modeling. Serve as a technical escalation point, influence roadmaps, implement AI-enabled workflows, and mentor engineers through hands-on architecture, coding, reviews, and technical guidance.

Owns Pfizer GH&B’s AI-ready Snowflake data warehouse, ontologies, data governance, quality, lineage, access controls, and production reliability. The role supports AI/ML data readiness, dashboards, prediction models, and critical data applications while establishing testing, release, documentation, and incident processes. It partners with enterprise technology and business stakeholders, directs contingent contractors, and influences governance and architecture decisions without direct people management.