Top Data Engineer Jobs in Richmond, VA

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

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design, build, and optimize BigQuery-based data solutions and data models. Manage data storage, partitioning, clustering, ETL, and data quality. Collaborate with architects, data scientists, and engineers to deliver scalable, secure cloud data infrastructure and documentation; senior hires provide technical leadership, code review, and process improvement.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Lead and mentor engineering teams to design, build, test, and support cloud-based data solutions. Work with big data, streaming, and data warehousing technologies, collaborate with product managers, and ensure performant, well-tested code and architectures.

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.

Lead design, develop, test, implement and support cloud-based data publication and transformation solutions. Collaborate across Agile teams, work with distributed systems, streaming, data warehousing (Redshift/Snowflake), RDBMS/NoSQL, mentor engineers, and ensure code quality and performance.

As a Senior Lead Data Engineer, you'll lead development teams, design technical solutions, utilize big data and cloud technologies, and drive transformation initiatives.

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 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.

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

Design, implement, and support interfaces, ETL and accounting systems. Develop object-oriented and scripting solutions, automate jobs, manage production operations, perform troubleshooting and root-cause analysis, and create technical documentation and test plans. Collaborate with project, support, and business analysts and support investments functions.

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.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

As a Data Engineer, you'll deliver scalable data solutions, participate in technical discussions, and ensure timely, trustworthy data delivery through robust pipelines.

Lead Data Engineer responsible for building technical solutions, collaborating with Agile teams, utilizing big data technologies, and developing cloud-based data warehousing solutions to enhance user experiences.

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.

Lead end-to-end migration of document-heavy legacy systems to cloud, converting unstructured records into analytics-ready formats, ensuring data quality and governance. Build and maintain MuleSoft integrations and APIs, apply data transformation (DataWeave), support Salesforce Data 360 implementation, and ensure lineage, metadata, and compliance while collaborating in Agile teams for phased cutovers and audit readiness.

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 and mentor engineering teams to design, build, test, and support cloud-based data platforms and streaming solutions. Develop data pipelines using big-data and cloud technologies, ensure code quality through testing and reviews, collaborate with product and digital teams, and drive People Tech transformation across HR and corporate functions.

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.

Build user-facing data products by transforming internal data into actionable metrics. Collaborate with platform teams and data scientists to prototype, validate, and prioritize data sources. Write requirements and proposals, engage in user research, and deliver performant Python/SQL-based data features using Databricks and Postgres.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.