Top Data & Analytics Jobs in Richmond, VA

Lead and scale companywide analytics for a DTC marketing business: own marketing measurement, financial forecasting, retention/LTV modeling, data architecture, reporting, and the analytics team. Set analytics strategy, oversee data engineering roadmap, build dashboards, apply statistical and ML methods, and translate insights into executive-level decisions.

Analyzes paid media performance across the full funnel, connecting spend and platform metrics to applications, enrollment, student lifetime value, and ROAS. Builds attribution frameworks, Databricks data pipelines, Power BI dashboards, semantic models, and advanced DAX reporting. Supports marketing mix modeling, experimentation, web analytics data quality, forecasting, governance, and workflow automation. Translates complex analytics into strategic recommendations for executive and cross-functional stakeholders.

Leads enterprise data platform architecture, engineering standards, technical decisions, and development of scalable data products. Designs batch and streaming systems, governance, observability, security, and reliability practices. Partners cross-functionally, mentors engineers, drives modernization and migrations, and establishes reusable patterns for data modeling, orchestration, quality, and platform operations.

Leads Relation Insurance’s enterprise data engineering and analytics function, owning data strategy, architecture, pipelines, governance, quality, reporting, and AI-enabled workflows. The role requires hands-on development in SQL, Python, and PySpark, executive communication, technical team leadership, lakehouse architecture, Power BI modeling, production support, and secure data management. The director also directs AI coding agents, builds LLM-powered workflows, establishes engineering standards, and delivers reliable data products aligned with business priorities.

Conduct pretesting, item analysis, IRT, score maintenance, data extraction, statistical reporting, and quality control for SAT assessments. Develop and maintain Python and R-based data management and statistical workflows, improve operational processes, document procedures, and evaluate AI-enabled tools. Collaborate with psychometricians and cross-functional teams to deliver accurate assessment data and reports under strict deadlines.

Collects, processes, and analyzes data from multiple sources using R, Python, and SQL. Develops reports, visualizations, analytical methods, and data-driven insights while applying statistical and introductory machine learning techniques. Collaborates with internal and external stakeholders, documents project progress in JIRA, debugs code, clarifies requirements, and communicates findings through reports and presentations.

Supports public health data extraction, management, cleaning, validation, harmonization, statistical and qualitative analysis, and evaluation. Develops analytic datasets and documentation, analyzes implementation and outcome measures, integrates mixed-methods data, and prepares technical memoranda, reports, tables, figures, and presentations. The role also uses Microsoft Office and CDC administrative systems while supporting government program monitoring and evaluation activities.

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

Partners with business leaders to analyze performance metrics, funding, compliance, enrollment, and retention data. Builds scalable analytical models, datasets, reports, dashboards, and forecasting tools using Python and SQL. Translates complex business rules into structured calculations, validates analytical outputs, identifies trends and risks, and delivers actionable insights supporting strategic, financial, and operational decisions.

Build and operate trusted finance data products, pipelines, models, reconciliations, dashboards, and automation. Partner with Finance, Accounting, Data Engineering, Product, and Platform teams to resolve data issues, improve reporting, support financial close, establish data lake quality controls, and enable timely insights. Use SQL, Python, dbt, Spark, Airflow, and AI-assisted tools to deliver tested, traceable, production-ready solutions.