Top Senior Data & Analytics Jobs in Richmond, VA

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights; manage people, mentor staff, partner with product and engineering, and build accessible data products and measurement practices.

The Senior Data Engineer will construct and optimize data pipelines, work with cross-functional teams, ensure data quality and governance, and mentor junior engineers while leading advanced analytics initiatives.

Design, build, and operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.

The Senior Enterprise Performance Analyst will develop dashboards, analyze performance data, support decision-making, mentor junior analysts, and improve organizational performance.

The Senior Product Analyst will automate data collection, analyze product metrics, conduct A/B tests, and support product hypothesis generation.

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 product analytics: build and maintain dbt models and Looker dashboards, define KPIs, analyze funnel and user journeys, design and evaluate A/B tests, size roadmap impact, and deliver partner and ad-hoc insights to inform product and executive decisions.

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.

Build, operate, and optimize large-scale data pipelines and warehouse schemas. Own Airflow orchestration, AWS-based data security (Lake Formation), automated data quality, and production incident resolution. Partner with stakeholders, mentor engineers, and drive DataOps best practices for performance, cost efficiency, and governance.

QA/Business Analyst responsible for developing and executing automated and manual tests, validating ETL/data pipelines and integrations, querying complex relational databases, using AI tools for anomaly detection and test generation, leading requirements gathering and maintaining traceability, documenting defects and data quality findings, and escalating critical issues to leadership.