Top Data Engineer Jobs in Richmond, VA

Lead and manage delivery of modern data ecosystems and managed data analytics programs. Oversee project planning, budgeting, quality, and client accounts. Mentor teams, drive data strategy, convert insights to business opportunities, and ensure standards and risk management across large transformations.

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

Develop and implement data solutions, pipelines, architectures, analytics, reports, and dashboards for clients. Apply Python, Java, ETL, business intelligence, and advanced analytics techniques to interpret complex data, improve operations, and support strategic decisions. Collaborate with stakeholders, manage client relationships, identify data challenges, and deliver high-quality managed services work while contributing to continuous process improvement.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Develop and maintain data pipelines and ETL processes, optimize SQL and data architectures, build BI reports and dashboards (Oracle BI, QlikView, Spotfire), analyze data to produce strategic insights, support client engagements in regulated environments, and apply continuous process improvements to ensure data quality and integrity.

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/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Lead Data Engineer, you will collaborate with Agile teams, develop technical solutions, and enhance cloud-based data warehousing to empower financial services for customers.

Lead and mentor a data engineering team to design, build, test, and deploy cloud-based, AI-enhanced risk management data solutions. Collaborate across Agile teams and product managers, implement big-data and streaming systems, perform unit tests and code reviews, and drive technology adoption and performance tuning.

As a Lead Data Engineer, you will design and implement data solutions using big data technologies, collaborate with Agile teams, and mentor other engineers.

As a Senior Lead Data Engineer, you will design, develop, and support data solutions, optimize systems, and collaborate within Agile teams to leverage technology for business transformations.

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.

Designs and maintains production data pipelines, dbt models, Airflow workflows, and Snowflake datasets integrating Workday and other People systems. Owns data quality, observability, governance, row-level security, and access controls for sensitive People data. Builds semantic models and AI agents, collaborates with analysts and stakeholders, translates requirements into reliable data products, communicates technical trade-offs, and mentors other engineers.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data solutions for Risk Tech. Collaborate with Agile teams to build distributed, real-time data and streaming systems using big data tools, data warehouses, RDBMS/NoSQL, and mentor engineering peers while ensuring performance and code quality.

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.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Lead design, develop, test, implement, and support cloud-based data solutions. Collaborate across Agile teams on distributed systems, streaming, data warehousing, and big data tooling. Mentor engineers, perform unit tests and code reviews, and deliver scalable data platforms to support ML and product teams.

Lead Data Engineer designing, developing, testing, and supporting cloud-based data solutions. Collaborate with Agile teams on distributed microservices, machine learning pipelines, data warehousing, streaming, and RDBMS/NoSQL systems. Mentor engineers, review and unit-test code, and deliver performant solutions using cloud and big-data technologies.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

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.