Top Senior Data & Analytics Jobs in Sacramento, CA

Lead and scale the enterprise data hub: manage the data warehouse, build reliable ingestion and transformation pipelines, implement governance and data modeling best practices, deliver GTM-ready metrics, enable AI/ML use cases, prioritize the data roadmap, and lead/mentor a data engineering team.

Own and evolve the data platform: architect, build, and maintain data models, pipelines, and access layers. Ensure data quality, governance, and reliability; monitor and optimize infrastructure; provide technical leadership, code reviews, and collaborate with cross-functional teams to deliver scalable data solutions for operations and analytics.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

Lead integration of technology into information systems, design data warehouse and BI capabilities, define data architecture and dictionaries, centralize disparate data, design dashboards, document system architecture, perform data quality and management, and provide solution engineering, pilots, and enablement for analytics and resource management.

As a Senior Data Scientist, you will develop statistical models and machine learning algorithms to improve patient enrollment and trial management while ensuring compliance in a healthcare data environment.

Use statistical modeling and data analysis to generate actionable insights that improve loan origination and production. Collaborate cross-functionally to design, test, and implement solutions, build dashboards and visualizations, maintain data integrity, and communicate findings to stakeholders to inform business strategy.

Build and own the quality analytics data layer: design and maintain dbt models, data marts, and Python-driven analyses. Define and standardize quality metrics, run experiments and A/B tests, support decision-making with dashboards and reports, automate recurring reporting, and partner with Quality and Engineering to ensure reliable, documented analytics.