Top Data Engineer Jobs in Richmond, VA

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Build scalable, privacy-focused data frameworks and ETL pipelines using big-data technologies (Spark, Presto, Flink, Kafka, Hive). Ensure appropriate handling of personal data, support privacy and legal needs, partner cross-functionally, and lead system design while mentoring teammates.

Build and maintain scalable data pipelines and integrations using Databricks and Azure. Transform and model large structured and unstructured datasets to support reporting, analytics, and operational needs. Implement data quality, monitoring, and governance; troubleshoot pipeline performance; collaborate with stakeholders and mentor junior engineers.

Lead and mentor a data engineering team to design, build, test, and support cloud-based, real-time data platforms and distributed microservices using modern big data, streaming, and data warehousing technologies to deliver scalable customer and enterprise experiences.

Lead Data Engineer responsible for designing data models, implementing data solutions, managing data pipelines, and collaborating with teams to drive technical initiatives.

Lead design, develop, test, deploy and support cloud-based data engineering solutions. Work across Agile teams on distributed, real-time and batch data platforms, mentor engineers, and optimize code and data pipelines using big data, streaming, and cloud data warehousing technologies.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Lead design and implementation of scalable cloud data lakes, warehouses, and marts. Build ELT/ETL pipelines, apply Data Vault 2.0 and dimensional modeling, implement data quality, lineage, observability, and governance. Optimize Snowflake/Databricks/Synapse platforms, champion CI/CD and DataOps, mentor engineers, and enable AI/ML-ready data products through collaboration with architecture and product teams.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Support building and maintaining ETL/ELT data pipelines and cloud data workflows. Work with senior engineers and cross-functional teams to implement, test, deploy, and document data processes. Continuously learn cloud technologies, SQL, and Python/PySpark, follow coding best practices, and contribute to process improvements in an Agile environment.

Design, build, and maintain scalable data pipelines and ETL/ELT frameworks using Python and PySpark. Develop reusable components, microservices, and cloud-based data solutions on AWS and Snowflake. Ensure code quality via reviews, CI/CD, logging, and best practices while collaborating with stakeholders to solve data problems.

As a Senior Lead Data Engineer, you'll lead a development team, design and implement data solutions using machine learning, distributed systems, and full-stack technologies, while collaborating with Agile teams and ensuring code quality.

Lead Data Engineer responsible for designing, building, testing, and supporting scalable data pipelines and cloud-based solutions. Collaborate across Agile teams to optimize data models, ensure data quality and governance, mentor engineers, and apply distributed computing, streaming, and emerging GenAI tooling to deliver personalized marketing and messaging experiences.

Lead Data Engineer responsible for designing, building, and supporting scalable cloud data pipelines and platforms. Collaborate across Agile teams to implement distributed computing, data modeling, quality, governance, and real-time streaming solutions using Python, SQL, Snowflake/Databricks, Spark, Kafka, and cloud services. Mentor engineers, participate in code reviews, and drive technical innovation including GenAI tooling.

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

Lead architecture and delivery of a cloud-native, petabyte-scale data platform: define technical roadmap, mentor engineers, build ETL/ELT pipelines, enable real-time streaming, implement Iceberg table format, optimize Snowflake/Redshift/Athena compute, and integrate observability with Splunk.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Build and maintain ingestion pipelines, Python services, and dbt models on AWS and Snowflake. Own operational on-call responsibilities, migrate pipelines to Airflow, contribute to CI/CD, collaborate with BI/Analytics, and mentor junior teammates.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, and maintain scalable data pipelines on AWS using EMR, Spark, and PySpark. Optimize EMR clusters and Spark jobs, implement ETL/ELT processes, ensure data quality, automate ingestion, and collaborate with data scientists and analysts. Document infrastructure, participate in code reviews, and monitor performance and costs.

Design, build, and maintain operational and analytical data platform capabilities using Java Spring Batch, Python, AWS, Oracle, and Snowflake. Deliver end-to-end development, production rollout, and support; improve financial product data quality, modeling, and SQL performance; collaborate with business partners and squads.

Design, build, and maintain ETL/ELT data pipelines and orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, and document processes while collaborating with cross-functional teams and supporting large-scale distributed data systems.

Design, build, and maintain scalable batch and real-time data pipelines and platform components. Produce curated datasets, ensure data quality, observability, and lineage, and collaborate cross-functionally to deliver data products that support analytics, experimentation, and ML.