Top Remote Data & Analytics Jobs in Sacramento, CA

Analyze user behavior, feature adoption, and retention across a B2B SaaS platform to generate evidence-based product insights. Track journeys, run cohort and funnel analyses, support A/B testing, define product KPIs, ensure data accuracy, and provide structured recommendations to inform roadmap and improve adoption and retention.

Lead end-to-end quantitative research projects for assigned clients: scope, design surveys, define samples, analyze data, and deliver client-ready insights and recommendations. Serve as primary client contact, guide clients to full-service or DIY solutions on Numerator's platform, use AI-enabled tools responsibly, mentor analysts, support sales and consulting efforts, and manage multiple projects while protecting scope, quality, and timelines. Travel as required.

Lead day-to-day management of multi-year data projects (Neighborhood Vitality Index, Hope Starts Here). Conduct quantitative and qualitative analysis, design and analyze surveys, coordinate with external partners and internal teams, perform stakeholder outreach and engagement, and produce accessible data products and communications for non-technical audiences to inform decision-making across Detroit-area organizations.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Lead architecture and evolution of Cardlytics' AWS-based data compute platform and lakehouse. Define standards for batch, streaming, ML, and AI workloads; build semantic layer, feature store, and governance. Partner with analysts and engineers, drive CI/CD (Terraform, GitHub Actions), monitoring, security, cost governance, and incident response while mentoring teams and shaping platform strategy.

Lead and own the population health analytics pipeline: intake and reconciliation of claims, eligibility, and MAO-004 data; identify and reconcile care gaps and closures; deliver practice-level performance analysis and billing reconciliations; build funnel instrumentation; document, standardize, and automate pipelines with Product and Engineering. Ensure billing-grade accuracy and support program measurement aligned to CMS reconciliation windows.

Design, build, and maintain scalable batch and streaming data pipelines, improve data modeling and governance, contribute to enterprise data architecture, and collaborate with cross-functional teams to ensure reliable, high-quality data for analytics and operational use.

Own portfolio monitoring and forecasting across card and loan products. Build executive dashboards and forecasting frameworks, perform RCA and remediation, support loss forecasting and provisioning, and collaborate with Risk, Finance, Capital Management, Engineering, and Accounting to inform strategy and lender negotiations.

We are seeking a Data Engineer to design, build and optimize scalable data pipelines. Strong experience in Databricks, PySpark, and data warehouse concepts is required.

Design and implement scalable, secure enterprise data solutions on Databricks and Azure using Delta Lake and Medallion Architecture. Lead and mentor data engineering teams; define integration patterns, governance, security (Unity Catalog, RBAC, masking, row/column-level security), and CI/CD/IaC practices. Ensure data quality, lineage, monitoring, and optimize Spark/Delta workloads for performance and cost while aligning solutions with business needs.