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

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Lead design and implementation of an enterprise Databricks lakehouse, build scalable batch and streaming pipelines, enforce governance and CI/CD standards, optimize Spark workloads, operationalize ML with MLflow, manage cloud infrastructure and IaC, and mentor data engineering teams.

The Staff Data Architect will enhance Jellyfish's data platform through architectural design, automation, advanced observability, and efficient data governance, while collaborating across teams.

Own BI analytics using a modern data stack to generate insights, build dashboards and self-serve tools, partner with stakeholders, and deploy AI-driven reporting and natural-language query solutions.

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 retail analytics to identify growth and efficiency opportunities, build strategic recommendations (store footprint, pricing, omnichannel), conduct market/competitive research, drive cross-functional initiatives, and deliver executive-ready analyses and frameworks to inform retail and digital strategy.

Lead and scale data subject rights and data lifecycle programs. Partner with Engineering/Product to design data discovery, retrieval, and deletion solutions. Build SQL queries, scripts, and dashboards; map data lineage; operationalize privacy controls; and improve tooling, automation, and reporting to ensure compliance with global privacy laws.

Design, build, and optimize BigQuery-based data solutions and data models. Manage data storage, partitioning, clustering, ETL, and data quality. Collaborate with architects, data scientists, and engineers to deliver scalable, secure cloud data infrastructure and documentation; senior hires provide technical leadership, code review, and process improvement.

Own implementation, maintenance, and optimization of client eligibility and claims data feeds. Analyze feed performance, troubleshoot discrepancies, produce error reports, test integrations with external partners, and improve internal tools, documentation, and processes to increase automation and reliability.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.