Top Data & Analytics Jobs in Richmond, VA

The Senior Analytics Engineering Manager will oversee analytics engineering vision, manage a team, ensure data quality, and drive data-driven decisions across departments.

As a Quantitative Researcher, apply economic and statistical models to large-scale data, collaborating to solve quantitative challenges across industries.

Lead people-analytics strategy for Talent Acquisition and Talent Development. Drive end-to-end analysis of complex workforce problems, deliver evidence-based recommendations to senior leaders, ensure data governance and quality, coach junior analysts, build metrics frameworks, and conduct benchmarking and external research to inform workforce strategy.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.

Lead measurement strategy for a book of clients, designing statistical models (MMM), experiments (MMT), and customer analyses. Use R, Python, SQL and ETL tools to extract insights, present advanced methodologies to stakeholders, provide strategic recommendations tied to business goals, mentor measurement analysts, drive process efficiencies, and leverage proprietary Nova tools and AI to optimize deliverables.

Lead design, development, and migration of enterprise SAP BW solutions to SAP Datasphere; architect hybrid data flows with Microsoft Fabric and Azure Synapse; integrate BW with Salesforce and SQL Server; embed AI (Copilot, Azure OpenAI) into analytics; manage deliverables, mentor offshore teams, enforce standards, and ensure data integrity and performance for reporting and analytics.

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

Lead design and scaling of ELT pipelines and analytics schemas on Snowflake. Build Looker views, CI/CD automation, and orchestration workflows (Prefect/Airflow). Ensure production reliability with monitoring (Datadog, Monte Carlo), partner with cross-functional teams, and provide technical leadership including code reviews, testing, and end-to-end ownership of data integrity and platform trustworthiness.

The Data Engineer designs, builds, and optimizes enterprise data pipelines, lakes, warehouses, integrations, and analytics infrastructure. Responsibilities include ELT development using Snowflake and Informatica, data governance, quality, security, performance monitoring, PII protection, cost optimization, and support for analysts and data scientists. The role also delivers process improvements, analytics tools, stakeholder collaboration, technical mentoring, and scalable data architecture across cross-functional teams.

Leads data engineering strategy, architecture, delivery, support, and lifecycle management for secure, scalable cloud and hosted data solutions. Collaborates with technical and business stakeholders to deliver complex datasets, establish enterprise data standards, manage data engineering teams, and oversee maintenance, change management, capacity planning, business continuity, disaster recovery, and regulatory security requirements. The role also evaluates third-party tools and supports Azure DevOps, SDLC, CI/CD, and technical innovation.