Top Data & Analytics Jobs in Richmond, VA

Lead enterprise data engineering efforts supporting defense missions. Responsibilities include prioritizing technical tasks, mentoring engineers, conducting code reviews, translating government requirements into scalable data architectures, developing Databricks ETL/ELT pipelines with Python, PySpark, and SQL, troubleshooting pipeline performance and reliability, and documenting systems. The role collaborates with government stakeholders, product owners, data scientists, analysts, and technical teams to deliver secure, high-throughput data platforms and actionable insights.

Designs and optimizes enterprise data models and Power BI semantic layers for reporting, forecasting, self-service analytics, and AI use cases. Develops dimensional models, DAX, SQL, and Power Query solutions; integrates enterprise data sources; validates data quality; optimizes performance; implements security and governance; and supports production datasets. Partners with technical and business stakeholders on requirements, documentation, metric definitions, deployment practices, and analytical solutions.

Designs and automates data validation, profiling, testing, monitoring, and quality frameworks for multi-source cloud data. The role uses Azure Synapse, Databricks, SQL, Python, Scala, Collibra, and Profisee to detect anomalies, enforce governance, and improve data reliability. Responsibilities include developing automated test plans, analyzing structured and unstructured datasets, supporting Azure data engineering workflows, and collaborating with engineers, analysts, data scientists, and product teams. A Public Trust or Suitability/Fitness determination may be required.

Leads business intelligence strategy and analytics delivery for conversational channels including IVR, IVA, and AI-powered engagement. Partners with product, engineering, operations, and business leaders to define KPIs, measurement frameworks, roadmaps, and success criteria. Mentors BI engineers, guides Agile delivery, develops Power BI dashboards and scorecards, analyzes conversational performance, and presents recommendations to senior executives.

Leads the design and delivery of enterprise-scale data, analytics, AI, Microsoft Fabric, Power BI, and data-driven application platforms. Owns analytics architecture, semantic modeling, data engineering, AI integration, governance, security, engineering standards, and technical roadmaps. Drives measurable business impact through KPIs and insights while leading architecture reviews, developing reusable patterns, and building analytics engineering, BI, data science, and technical leadership teams.

Serve as the primary business intelligence and analytics partner for Governance, Risk & Assurance and the General Counsel Strategy Office. Analyze complex datasets, build dashboards and data models, support data governance and risk initiatives, identify automation and AI opportunities, and translate technical findings into business recommendations. Collaborate across enterprise functions, provide reporting and technology guidance, evaluate AI solutions, and drive adoption of data-informed practices.

Designs and maintains enterprise data architecture, including data lakes, warehouses, integrations, migrations, orchestration, and data management protocols. Partners with architects, DBAs, BI teams, product leaders, and business stakeholders to support analytics and data science. Requires deep experience with cloud data environments, Azure SQL, Microsoft Fabric, Spark, Hadoop, PySpark, SparkSQL, security protocols, and enterprise data integrations.

Maintains data governance and quality across mission systems by validating sources, managing metadata and data dictionaries, reconciling metrics, performing profiling and cleansing, documenting lineage, and supporting cross-system harmonization. The analyst collaborates with reporting teams, dashboard developers, data owners, and PMO leadership to ensure accurate, standardized, compliant enterprise reporting.

Own product, content, subscription, and growth analytics for adult product lines. Analyze conversion, retention, churn, cohorts, and LTV; maintain reporting; create measurement plans; support experimentation; and deliver recommendations to Product, Engineering, Content, and Design. Use SQL, visualization tools, statistical methods, and AI-assisted workflows while validating results and documenting work.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.