Top Senior Data & Analytics Jobs in Richmond, VA

Perform actuarial analytics and forecasting to model financial and market trends, support value-based care contracting and implementation, ensure data integrity, develop metrics and forecasts, troubleshoot data issues, and advise leadership to inform strategic and operational decisions.

Builds and manages ETL processes and data pipelines in Azure Databricks using PySpark. Integrates data from multiple sources, tunes Spark and SQL for performance, troubleshoots pipeline failures, automates manual processes, and supports reporting and analytics to inform business decisions.

Lead analytics for Wealth Solutions: analyze client segmentation, revenue, and operational data; build models for capacity and workforce planning; produce insights and recommendations for senior leadership; partner with BI and Finance to improve advisor productivity, client experience, retention, and revenue growth; support strategic projects and translate complex data into clear presentations.

Support and develop BI applications for a client financial platform across environments. Build reports, analytics, and budgeting solutions (TM1), develop/tune Oracle SQL/PLSQL, deploy BI tooling, support UIs (low-code), follow Agile practices, and collaborate on integrations, security hardening, and operations.

Lead design and governance of enterprise Master Data Management using Stibo/Profisee and cloud platforms. Define multi-domain data models, match & merge strategies, integrations with ERP/CRM/eCommerce/analytics, stewardship workflows, data quality, lineage, RBAC, and scalability. Partner across business and IT to operationalize MDM capabilities and enable data governance.

Lead end-to-end product analytics: instrumentation, data quality, funnels, retention, and experimentation. Use SQL and Python for analysis, build dashboards, partner with product and growth, and improve analytics infrastructure (Amplitude and data warehouse migration). Remote with GMT+1 overlap.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

Lead a hands-on data engineering team to build and scale production data pipelines, datasets, and observability for alternative-data products. Drive architecture, data modeling, QA, and AI-ready dataset design while coaching engineers, partnering with product and research, and owning operational excellence across Databricks, Airflow, SQL, and PySpark.

Lead design and scaling of backend data infrastructure for Beacon's Datastore: build event-driven pipelines, data models, and APIs; draft RFCs; integrate services; improve operational robustness, documentation, and tooling to support clinical and scientific workflows.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Lead and grow SQUIRE's Data organization as a player-coach, owning long-term data architecture, building reliable Snowflake pipelines, setting standards for modeling and data quality, hiring and budgeting for a 5+ person team, partnering cross-functionally, auditing integrations, and enabling AI/LLM use cases while remaining hands-on for high-risk technical work.