Top Data Engineer Jobs in Richmond, VA

As a Data Engineer, you will design and maintain data pipelines, build data storage systems, and support analytics workflows, ensuring data reliability and performance. You will collaborate with engineers to define data structures and improve data management practices.

Design, develop, test, implement, and support cloud-based data solutions. Collaborate across Agile teams on distributed, streaming, and full-stack systems, mentor engineers, and perform unit tests and code reviews to deliver performant data products.

Design, develop, test, and support cloud-based data solutions and streaming systems. Collaborate with Agile teams, mentor engineers, and ensure performant, well-tested code for large-scale financial data workloads.

Lead and mentor engineering teams to design, develop, test, and deploy cloud-based data solutions. Build distributed, real-time and batch data pipelines using big data technologies, databases, and cloud warehouses. Collaborate with product managers and peers to deliver performant, production-ready systems and ensure code quality through testing and reviews.

Lead Data Engineer designing, developing, testing, and supporting cloud-based data solutions. Collaborate across Agile teams to build distributed, streaming, and batch data pipelines, mentor engineers, and optimize performance for data warehousing and machine learning use cases.

Lead design, develop, test, implement, and support cloud-based data solutions. Collaborate across Agile teams on distributed, streaming, and full-stack systems; perform unit testing, code reviews, mentoring, and drive adoption of modern data warehousing and big-data technologies.

Lead cross-functional Agile teams to design, build, test, and support cloud-based data solutions. Develop full-stack data engineering systems using Java/Scala/Python, RDBMS and NoSQL, Redshift/Snowflake, and distributed tools (Hadoop, Spark, Kafka). Mentor engineers, perform code reviews and unit testing, and collaborate with product managers to deliver scalable, performant data products.

Design, develop, test, implement, and support cloud-based data solutions; collaborate across Agile teams; build distributed, streaming, and data warehousing systems; write and review performant code; mentor engineers and stay current with emerging technologies.

Design, build, test, and support cloud-based data solutions; collaborate with Agile teams and product managers; develop full-stack and distributed data systems using big-data, streaming, and warehousing technologies; perform unit tests and code reviews; mentor peers and stay current with emerging technologies.

Design, build, test, and support cloud-based data solutions and streaming applications. Collaborate with Agile teams, mentor engineers, implement distributed computing and data warehousing (Redshift/Snowflake), and ensure performant, well-tested code for production systems.

Design, develop, test, implement, and support cloud-based data solutions. Collaborate with Agile teams on distributed, real-time, and full-stack systems; write and review performant code; mentor engineers and adopt emerging data technologies.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

Lead and mentor multiple Databricks-focused data engineering teams, define engineering best practices and reusable frameworks, drive CI/CD and performance/cost optimization, build and support analytical and streaming pipelines, and collaborate with architects, product owners, and cross-functional partners to deliver high-quality data products.

The Data Engineer will design and maintain ETL pipelines and data storage solutions, collaborating with cross-functional teams to ensure efficient data processing and delivery.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

Design, develop, and maintain data pipelines and workflows; perform data integration, transformation, and cleaning; translate business requirements into technical solutions; support and troubleshoot 100+ pipelines; participate in code reviews, documentation, reporting, and ensure healthcare data privacy and security compliance.

Design, build, and optimize distributed query solutions using Starburst/Trino. Enable secure, governed data access across cloud and on-premises sources, implement data federation and query optimization, support IAM and governance in a federal environment, develop backend services and CI/CD, and troubleshoot performance in cloud-native systems.

Design, build, monitor, and optimize large-scale ETL/ELT data pipelines and data architecture. Integrate data sources, support analysts and data scientists, automate processes, ensure reliable data delivery, and write maintainable code.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

The Data Engineer will manage data extraction pipelines, modernize data systems, implement monitoring tools, optimize workflows, and support compliance requirements using various tools and platforms.

As a Data Engineer, you will build and optimize data pipelines, manage ETL workflows, and ensure efficient data access and quality within the infrastructure.

As Lead Data Engineer, you will guide a team, manage the data platform, and oversee the development of ML infrastructure and feature stores.

Build and operate scalable data pipelines and analytics-ready datasets using Python, dbt, and BigQuery. Develop Looker dashboards, ensure data quality and observability, optimize performance and cost, integrate data workflows with backend services and CI/CD, and support analytics, experimentation, and production use cases.

Design, implement, optimize, and maintain scalable, high-availability database solutions for a SaaS healthcare product. Integrate client data, resolve complex data issues, refactor systems for Azure, optimize MSSQL performance, participate in code reviews, and deliver features within Agile/Scrum to ensure reliable, high-performance data services.

Design, build, and support scalable ETL/ELT data pipelines across IBM Netezza, DataStage and modern cloud platforms (Snowflake, Databricks). Develop complex SQL, troubleshoot production pipelines, and collaborate with BI and cloud teams for data ingestion, transformation, validation, and documentation.