Top Data & Analytics Jobs in Sacramento, CA

Own end-to-end data pipelines for multiple clients: extract, clean, transform, model and visualize data. Build dashboards and automated workflows, apply AI where useful, and present actionable insights to non-technical stakeholders.

The Senior Analytics Engineer will design and build scalable data pipelines using dbt and Snowflake, providing technical guidance, conducting audits, and mentoring junior engineers while leveraging AI tools to enhance productivity.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

The Lead Data Platform Engineer will design and build Virtuous's data platform, ensuring it is secure, scalable, and AI-ready. Responsibilities include architecting access controls, optimizing performance, and enabling self-service analytics while maintaining governance and compliance standards.

As a Salesforce Technical Architect focused on Data & AI, you'll design and implement solutions using Salesforce Data 360, oversee technical projects, collaborate with various stakeholders, and mentor junior staff to drive AI innovations.

Develop and maintain Epic Healthy Planet registries and analytics solutions: gather requirements, profile Epic source data, build rules/metrics/dashboards, provide ad-hoc analysis, support end users, document solutions, and mentor users.

The Data Engineer will develop and maintain data pipelines, build data models, ensure data quality, and support cross-functional teams while improving internal data platform infrastructure and documentation.

The Sr. FWA Analyst identifies fraud, waste, and abuse in medical billing through data analysis, supporting engineering and data science teams with validation and tuning of detection algorithms.

The Business Intelligence / Data Analyst will optimize the data reporting pipeline using Google Big Query and Omni, work with product management, implement reports, and conduct ongoing maintenance.

Lead enterprise data strategy and governance to establish semantic architecture, metadata, lineage, stewardship, and standards enabling trusted data for AI, analytics, and operations. Define operating model, policies, reference architectures, and roadmaps while partnering with Legal, Compliance, Security, Technology, and business leaders to ensure regulatory compliance and enterprise-wide adoption.