Top Data & Analytics Jobs in Sacramento, CA

Support sales intelligence by collecting, preparing, and analyzing sales and marketing data; maintain dashboards and data quality; run pipeline and win/loss analyses; translate findings into clear visualizations and reports for stakeholders.

The Senior Data Scientist will develop and deploy analytical models supporting marketing strategies, assess campaign effectiveness, and optimize customer engagement strategies, employing advanced data-driven methods and partnering with marketing stakeholders.

Maintain and improve eligibility and claims data feeds, analyze feed performance and errors, troubleshoot discrepancies, work with external feed providers to map and test integrations, and help improve internal tools, documentation, and processes.

Manage analytical service operations by overseeing a team, designing software for data mining, developing statistical models, and generating insights for marketing outcomes. Provide training and mentorship to direct reports.

Lead analytics implementations using Google Analytics and Tag Manager for clients, providing mentorship and managing projects with a focus on enhancing clients' digital strategies.

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

The Data Engineer will lead data projects, collaborate with stakeholders, develop data transformations, and maintain data quality for business needs.

The Analytics Engineer will manage BI modeling, create dashboards, ensure data quality, and collaborate with customers and teams for reporting needs.

The Market Risk Analyst will monitor SBL lines of credit, assess market risks, manage collateral, develop risk models, and prepare reports.

Lead the ML Data Enablement team to define strategy and execution for accelerating third-party and internal data onboarding, enforce data quality and SLAs, build metadata and lineage standards, and deliver scalable data infrastructure and workflows to reduce time-to-production for ML models.