Top Data & Analytics Jobs in Richmond, VA

Leverage data analytics, business intelligence, and Python to deliver strategic insights and tailored solutions for clients. Build reports, dashboards, data pipelines, and support data architecture and quality initiatives. Analyze data from multiple sources to identify trends, improve operations, and inform decision-making. Collaborate with stakeholders and team members while continuously developing technical skills and contributing to process improvements.

Analyze complex data, build self-service data solutions, and develop business intelligence tools, metrics, dashboards, and visualizations. Partner with business and technology teams to deliver strategic insights, manage data quality, metadata, lineage, definitions, access governance, and monitoring. The role requires scripting, data querying, visualization, AWS, data governance, and data quality experience.

Analyze complex data to support business strategy, build self-service data solutions, develop dashboards and metrics, and improve data governance and quality. The role partners with business and technology teams, manages data access and metadata, monitors data quality, and applies Python, R, Spark, SQL, AWS, and visualization tools across innovation, business intelligence, and data management initiatives.

Lead Data Engineer developing cloud-based data and AI solutions for risk management. Responsibilities include building, testing, deploying, and supporting full-stack systems; developing data pipelines, distributed and streaming applications; using cloud warehouses, RDBMS, NoSQL databases, and machine learning technologies; conducting code reviews and unit tests; collaborating across Agile teams; and mentoring engineers. The role requires application development, big data, and cloud experience, with preferred expertise in real-time data, data warehousing, distributed computing, and AI tooling.

Lead programmatic analytics for clients and internal teams by analyzing campaign and model performance, designing tests, developing measurement solutions, recommending optimizations, and presenting actionable insights. Partner with Sales, Customer Success, Product, Engineering, and clients to align marketing strategies, reporting requirements, and optimization efforts with business goals.

Leads the strategy, architecture, implementation, and evolution of enterprise analytics platforms. Responsibilities include designing scalable data platforms, integrating digital and enterprise data, establishing governance and security standards, optimizing analytics and BI capabilities, evaluating cloud technologies, managing platform performance and risks, and mentoring technical teams. The role also requires close collaboration with executives, clients, Product, Engineering, Data Science, Marketing, and business stakeholders.

Lead and scale Runpod’s analytics and business intelligence function, managing analysts and BI practitioners while setting the analytics roadmap. Own core company metrics, consumption and marketplace analytics, self-service reporting, experimentation practices, and executive and board-level insights. Partner with Product, Finance, Engineering, Security, Supply, and Go-to-Market leaders to improve data quality, modeling, decision-making, and strategic outcomes.

Build and own end-to-end data products, including pipelines, transformations, semantic models, dashboards, metrics, and AI-enabled workflows. Partner with business and technical stakeholders to turn ambiguous questions into trusted, maintainable solutions. Establish data quality testing, documentation, monitoring, and governance while improving architecture and automation. Use Snowflake, dbt, Power BI, and related technologies to support analytics across Marketing, Product, Finance, and Engineering.

Architect scalable data models and ELT pipelines using modern data lake technologies. Build self-service analytics products, establish enterprise data governance and observability standards, and lead data architecture across engineering teams. Partner with data scientists, product engineers, and business stakeholders to improve data literacy, identify root causes, and connect insights to business outcomes. Develop AI-enabled data tools and frameworks while providing technical leadership, mentorship, and direction for analytics engineering practices.

Design and maintain analytical data models, transformation frameworks, data-quality checks, semantic definitions, and metric consistency across analytics and customer-facing products. Partner with engineering, product, and analytics teams to document data, improve lineage, and enable trustworthy self-service data usage. The role requires advanced SQL, analytics engineering, dimensional modeling, testing, and collaborative translation of business concepts into precise data definitions.

Lead the architecture, development, and scaling of data pipelines and infrastructure for a customer-facing Data Lake platform. Build ETL and ingestion systems using GCP, Debezium, Pub/Sub, Dataflow, BigQuery, Python, Golang, and SQL. Establish engineering standards, deliver strategic projects, mentor engineers, influence cross-functional technical decisions, optimize data services, and shape the platform roadmap.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.