Top Senior Data & Analytics Jobs in Richmond, VA

Design, build, and maintain data solutions; master OneStream EPM capabilities; perform data analysis and visualization; ensure data quality, lineage, and governance; collaborate with tech teams on security and access; enable self-service analytics and drive business insights.

As a Senior Manager, Data Analysis, you will validate analytical tools, enhance technical skills, manage projects, and coach junior staff while collaborating cross-functionally.

Lead analytical and strategic work across marketing, product and credit risk: identify opportunities, build models, support product/pricing strategy, enable direct-to-consumer marketing, drive execution and cross-functional implementation, and monitor outcomes to improve profitability and customer engagement.

Lead design and implementation of enterprise Product Information Management solutions; oversee product data lifecycle, integration, and data quality; manage and mentor a team; drive client engagement and business development; analyze and improve processes and uphold deliverable quality and governance.

Lead design and implementation of enterprise Product Information Management (PIM) solutions, ensure product data quality and integration, drive business development and client engagement, mentor team members, improve processes and productivity, and align product data management with organizational objectives.

Lead and architect end-to-end Master Data Management (MDM) solutions, manage client relationships and project delivery, mentor and coach team members, ensure data quality and integration, and align MDM platforms with business processes to improve data stewardship, taxonomy design, and model harmonization.

Design, implement, and validate data mapping, transformation, and integration solutions for payment integrity. Investigate and resolve complex data quality issues, create mapping specs and documentation, partner with stakeholders, and mentor analysts to improve data quality and automation.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

As a Staff Product Analyst, you'll define analytics strategies, build measurement infrastructure, design experiments, and partner with product teams to boost self-serve conversion and product adoption.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

The Senior Analyst will support the personalization algorithm by conducting analyses, testing features, managing projects, and collaborating with teams to improve food recommendations for customers.

The Senior Product Analyst collaborates with Product Managers to analyze data, identify metrics, and provide recommendations to shape product strategy and improve customer experience.

Help new Dynatrace customers adopt Digital Experience capabilities (Synthetic Monitoring, RUM, Session Replay). Perform data-driven analysis, create concise enablement and investigation deliverables, prioritize customer onboarding activities, drive usage expansion, and collaborate with account teams while communicating findings to technical and business stakeholders.

Lead and scale enterprise data governance operations: manage the data domain model and business glossary, run governance council operations, enforce data policies and compliance (SOC 2, GDPR/CCPA), drive catalog adoption and data literacy, and measure governance maturity and ROI through dashboards and remediation workflows.

Transform data into actionable insights by building dashboards, reports, and visualizations. Partner with cross-functional teams and data engineering to ensure data quality, lineage, and AI-ready metadata. Audit datasets, define monitoring rules, document data dictionaries, pilot AI-assisted BI tools, and apply hypothesis-driven analysis to surface insights that inform business decisions.