Top Data Engineer Jobs in Hampton, VA

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Develop and maintain scalable data stores, pipelines, and ETL/ELT workflows that transform structured and unstructured data for analytics and reporting. Build and deploy data platforms, scripts, and batch or streaming solutions in collaboration with analysts, developers, data scientists, infrastructure engineers, and other stakeholders. Support platform scaling, monitoring, operations, and client-focused mission initiatives.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Staff Data Engineer leading architecture and technical direction for real-time and batch motorsports data platforms. Responsibilities include telemetry ingestion, streaming systems, data quality, observability, cloud platforms, reliability, incident response, technical standards, roadmap planning, stakeholder influence, and mentoring. The role delivers scalable solutions supporting race-day operations and cross-functional analytical products.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Build and deliver Azure and Microsoft Fabric data solutions for clients. Responsibilities include developing data pipelines with Azure Databricks, Data Factory, Synapse Analytics, and Fabric; writing SQL, Python, and PySpark; supporting data integration, testing, deployments, troubleshooting, CI/CD, and infrastructure management. The role involves consulting with client stakeholders, collaborating with architects and developers, and delivering modern data platforms and reporting solutions.

Leads enterprise data platform architecture, engineering standards, technical decisions, and development of scalable data products. Designs batch and streaming systems, governance, observability, security, and reliability practices. Partners cross-functionally, mentors engineers, drives modernization and migrations, and establishes reusable patterns for data modeling, orchestration, quality, and platform operations.

Designs and optimizes scalable data infrastructure, including ETL pipelines, data integrations, BI solutions, validation, cleansing, workflow optimization, and troubleshooting. The role supports clinical and operational teams by integrating data from clinical systems, APIs, warehouses, and external sources while ensuring data quality and performance. Collaboration across departments and delivery of data-driven insights are key responsibilities.

Design and maintain Snowflake-based data platforms, cloud-native data lakes, and scalable ETL/ELT pipelines for asset-backed finance analytics. Orchestrate workflows with Dagster and AWS services, ensure data reconciliation and integrity, and support migration from legacy systems. Contribute to project planning, Jira backlog management, testing, cutover planning, and cross-functional go-live readiness.

Design and maintain Snowflake data pipelines and data lake architectures for asset-based finance analytics. Develop ETL/ELT workflows, validate and reconcile financial data, migrate legacy systems, and translate quantitative finance logic into scalable architectures. Support project execution through requirements breakdown, planning, testing, cutover, and go-live readiness. Collaborate across engineering, quantitative analytics, and stakeholder teams.

Design, build, and operate production data pipelines and shared data foundations using SQL, Python, PySpark, Databricks, and cloud technologies. Develop data models, ingestion and transformation workflows, resilient processing, observability, data-quality controls, and secure customer-aware data flows. Collaborate with engineering and product teams, evaluate architecture and tooling, support analytics and application services, contribute to testing and documentation, and mentor other engineers.

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

Designs Azure-based data models, simulations, test environments, automated testing frameworks, and performance validation solutions for healthcare data pipelines. Develops test data generators, validates ETL and HL7 FHIR transformations, supports data quality and reliability, and collaborates with engineers and architects on design and code reviews. The role also provides technical guidance on testing architecture, CI/CD, infrastructure as code, resiliency, and large-scale cloud data processing.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Design, build, and maintain scalable batch and streaming data pipelines using Databricks, PySpark, Python, SQL, Spark, and Delta Lake. Develop transformations, orchestration workflows, data models, validation processes, and curated data products. Troubleshoot performance and quality issues, apply governance and security practices, document solutions, and collaborate with architects, data scientists, DevOps teams, analysts, and clients.

Develop and maintain scalable data platforms, data stores, and ETL/ELT pipelines that organize structured and unstructured data for analytics and mission-focused applications. Collaborate with analysts, developers, data scientists, infrastructure engineers, and data consumers in agile teams. Create scripts and programs to transform data, support platform operations, and deploy batch and streaming analytics solutions. The role requires Python, a Secret clearance, and a bachelor's degree.

Designs, builds, and maintains Snowflake-based enterprise data warehouse solutions, including data architecture, ETL/ELT pipelines, security, deployment, data quality, modeling, and performance optimization. Partners with integration, analytics, governance, business intelligence, and architecture teams. Supports production operations, resolves ingestion and transformation issues, develops AWS-to-Snowflake scripts, documents implementations, and translates business requirements into scalable data solutions.

Designs, develops, and maintains scalable ETL/ELT pipelines and data platform capabilities for high-volume healthcare data. Responsibilities include complex SQL development, Airflow orchestration, dbt transformations, data quality frameworks, pipeline monitoring, troubleshooting, performance optimization, and documentation. The role collaborates with Data Operations, Product, Analytics, Data Science, and clinical teams to deliver reliable data for analytics, reporting, product features, and AI/ML initiatives.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.