Top Data & Analytics Jobs in Sacramento, CA

Supports loyalty and retention analytics through A/B test management, KPI tracking, recurring dashboards, and performance reporting. Monitors churn, winback, cancellation, and loyalty metrics; identifies trends, risks, and opportunities; and translates findings into actionable recommendations. The role also manages loyalty merchandise planning and replenishment, collaborates with Data Science and program owners, coordinates analytics requests, and prepares MBR/QBR materials.

Lead enterprise data architecture across AWS and Databricks Lakehouse: design scalable data models, ensure governance and data quality, mentor teams, evaluate and adopt data technologies (Delta Lake, Unity Catalog), oversee large projects and deliverables, and incorporate generative AI to accelerate data modeling and delivery.

Support Numerator clients through ad hoc analyses, consumer-panel insights, platform guidance, reporting, and recommendations that improve sales and marketing effectiveness. The role requires understanding syndicated and household panel data, business intelligence platforms, client relationship management, presentations, and data-driven problem solving. It also involves developing expertise in Numerator solutions and sharing consumer behavior findings through industry content, forums, whitepapers, and blogs.

Performs geospatial data management, analysis, visualization, and analytical modeling for government and commercial clients. Supports hazard identification, risk assessment, and risk reduction efforts using ArcGIS and Microsoft tools. Develops web, mobile, and desktop applications; tests, deploys, documents, and maintains solutions. This non-full-time, on-call role requires frequent travel and deployment to disaster recovery sites nationwide.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Manage end-to-end data operations, including collection, processing, delivery, pipeline optimization, and data quality monitoring. Collaborate with engineering, product, and operations teams to improve data workflows, identify inefficiencies, and implement scalable solutions. Analyze data to provide actionable insights for decision-making and AI model development while communicating technical issues to non-technical stakeholders.

Designs, develops, and maintains scalable ETL pipelines, data lakes, warehouses, and integrated data solutions. The role includes requirements analysis, Agile collaboration, testing, debugging, documentation, cloud data engineering, and optimization of reliable and performant systems. It requires strong Python, SQL, database, distributed processing, data engineering, software development, and cloud platform skills, along with effective communication and problem-solving abilities.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

Build and extend automated enterprise data governance and quality frameworks. Define measurable trust rules, data contracts, validation and certification workflows, metadata, lineage, tagging, and trust scoring across a cloud data platform. Integrate governance checks into developer workflows and Collibra, coach engineering and product teams, and establish organization-wide data trust standards. The role requires strong SQL, BigQuery or comparable warehouse experience, and expertise with data quality or observability tools.

Analyzes, cleanses, validates, and prepares operational datasets for mailing, email, reporting, billing, and processing activities. Develops Databricks queries and Alteryx workflows to automate processes and improve data quality. Performs audits, reconciliations, root-cause analysis, and corrective actions while maintaining governance and security compliance. Provides actionable insights, mentors junior analysts, communicates findings, and leads continuous improvement initiatives.