Top Senior Data & Analytics Jobs in Sacramento, CA

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

The Senior Analyst will support the personalization algorithm by conducting analyses, testing features, managing projects, and collaborating with teams to improve food recommendations for customers.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Embed with the CRO to deliver GTM analytics and insights: write SQL and dbt models, build Looker and Salesforce dashboards, define and track revenue KPIs, forecast and model revenue, partner with GTM leaders and Data Engineering, lead analytics projects, and translate complex data into executive-ready recommendations to drive upsell and new-business growth.

The Senior Analytics Engineer will develop data pipelines, build models, and create dashboards to provide actionable insights for various teams. The role requires collaboration with multiple stakeholders to meet analytical needs and ensure data governance.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

Design and govern the enterprise semantic layer: architect reusable data models, manage hybrid (SSAS to Microsoft Fabric) modeling, enforce HIPAA-aligned security, optimize performance using DAX and VertiPaq analysis, implement analytics-as-code and CI/CD for models, define metric governance, and mentor junior engineers. Requires partial on-site work.