Top Senior Data Engineer Jobs in Richmond, VA

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance and security compliance, build client relationships, and improve cloud cost/performance.

Design and implement scalable data solutions and pipelines, build and manage advanced data ecosystems, analyze complex problems, mentor junior staff, maintain high deliverable standards, and build client relationships while applying data governance and analytics best practices.

Design, build, and maintain data infrastructure, pipelines, lakes/warehouses, and ETL processes using cloud and big-data technologies. Ensure data quality, security, and performance while collaborating with clients and supporting data strategy and analytics.

Lead design and delivery of cloud-based data architecture, ETL/ELT pipelines, data warehouses and lakes. Manage teams, coach associates, ensure data governance/security compliance, troubleshoot pipelines, and collaborate with stakeholders to translate business requirements into scalable technical solutions.

Lead design and implementation of data architecture and cloud-based data solutions. Develop data pipelines, models, and flow diagrams; ensure governance and security compliance. Lead large projects, advise clients strategically, drive operational excellence, and coach teams to solve complex problems.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

Lead technical strategy and architectural evolution in marketing technology, mentoring teams and driving innovative solutions across data engineering and platforms.

Lead data engineering engagements within managed services: design data architectures, build ETL pipelines, implement analytics and BI solutions (Redshift, DataStage, SQL/DB2), coach teams, engage stakeholders, apply systems thinking, validate client outcomes, and drive solution optimization and performance tuning.

Lead Data Engineer responsible for designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborate across Agile teams, work with distributed systems, big data and streaming tools, data warehousing, and mentor engineers. Ensure code quality via unit tests and performance tuning, and partner with product managers to deliver scalable data experiences.

Lead and mentor a data engineering team to design, develop, test, and support cloud-based big data and streaming solutions. Build full-stack data pipelines using Java/Scala/Python, RDBMS/NoSQL, Redshift/Snowflake, and cloud platforms. Collaborate with product managers, perform unit tests and code reviews, and promote engineering best practices and tech community engagement.

Lead and mentor data engineering teams to design, build, test, and support cloud-based, full-stack data solutions. Work with big data, streaming, and data warehousing technologies (Redshift/Snowflake), collaborate with product managers, and ensure high-quality, performant code and systems.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data publication and transformation solutions. Collaborate across Agile teams to build distributed, real-time data pipelines and data warehousing (Redshift/Snowflake), perform unit tests and code reviews, mentor engineers, and adopt emerging technologies to deliver scalable data platforms.

As a Sr. Lead Data Engineer, you'll collaborate across teams to develop technical solutions, lead developers, and utilize big data technologies and cloud services to support data transformations.

The role involves migrating on-premises databases to AWS, developing Python scripts for data processing, and building AWS data pipelines. Responsibilities include supporting application teams, data modeling, and ensuring system efficiency.

Lead and mentor a data engineering team to design, build, test, and support cloud-based data solutions. Develop distributed, real-time and batch data pipelines using Java/Scala/Python, NoSQL and RDBMS, and cloud data warehousing (Redshift/Snowflake). Collaborate with product managers and Agile teams to deliver performant, production-ready systems.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Lead and mentor a team of data engineers to design, develop, test, implement, and support cloud-based data platforms and full-stack solutions. Build distributed, real-time, and batch data systems using Java/Scala/Python, big data tools (Spark, Hadoop, Kafka), NoSQL and RDBMS, and data warehouses (Redshift, Snowflake). Collaborate with Agile teams, perform unit tests and code reviews, and drive technology experimentation and transformation.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.