Data Engineer

540 is seeking a Data Engineer to support a mission-critical technology modernization effort for the Department of War. You will design, build, and maintain Databricks-based data pipelines and lakehouse capabilities that enable secure data integration, analytics, AI/ML, and operational workloads at enterprise scale.

Working with software engineers, AI/ML engineers, cybersecurity teams, and mission stakeholders, you will build scalable and reliable solutions using Databricks, Python, Apache Spark, and Delta Lake. The ideal candidate enjoys solving complex engineering challenges and developing trusted data products that support national defense missions.

Location: Arlington, VA
Citizenship & Clearance Requirement: Per client requirements, candidates must be U.S. Citizens with an active DoW Secret (or higher) clearance
Education Requirement: Bachelor’s degree in Computer Science, Engineering, or a related technical field preferred; equivalent combinations of education and relevant experience will be considered
540 Internal Thrive Level: Data Engineer II or III

WHY 540?

540 is a forward-thinking company that the government turns to in order to #getshitdone. We don’t just talk about innovation – we deliver it. We break down barriers, build impactful technology, and solve mission-critical problems.

HOW YOU’LL DRIVE IMPACT

• Design, develop, and maintain Databricks-based data pipelines, data products, and lakehouse capabilities
• Build automated ETL/ELT pipelines that ingest, transform, and deliver mission-critical data
• Develop production-grade data-processing solutions using Python, SQL, PySpark, Apache Spark, and Delta Lake
• Design and maintain data models, schemas, tables, and medallion architecture patterns supporting analytical, operational, and AI/ML workloads
• Build and operate batch and streaming data pipelines supporting mission requirements
• Develop and manage Databricks notebooks, jobs, workflows, clusters, and compute resources
• Implement data-quality checks, automated testing, monitoring, lineage, and metadata-management capabilities
• Support data discovery, governance, and access controls using Unity Catalog or similar technologies
• Optimize Spark workloads and Databricks resources for performance, scalability, reliability, and cost efficiency
• Collaborate with engineers, analysts, and data scientists to deliver reusable data products and mission capabilities
• Support Databricks deployments using CI/CD, infrastructure as code, and source control
• Partner with cybersecurity teams to implement data-protection, access-control, auditing, and governance requirements
• Troubleshoot issues affecting Databricks workloads, data pipelines, storage systems, and production data services
• Document data models, pipeline designs, engineering processes, and operational procedures

REQUIRED SKILLS & EXPERIENCE

• 4+ years of relevant data engineering or software engineering experience
• Hands-on experience developing and operating production data pipelines using Databricks
• Proficiency with Python, SQL, PySpark, Apache Spark, and Delta Lake
• Experience building automated ETL/ELT pipelines for large-scale datasets
• Experience designing and maintaining data models, schemas, tables, and lakehouse architectures
• Experience managing Databricks notebooks, jobs, workflows, and compute resources
• Experience implementing data quality, automated testing, monitoring, lineage, or metadata-management capabilities
• Experience working with Databricks and cloud-based data services in AWS, Azure, or Google Cloud
• Experience working with structured, semi-structured, and unstructured data
• Understanding of lakehouse architecture, data governance, security, privacy, and access-control principles
• Ability to troubleshoot data pipelines, Spark workloads, infrastructure, and applications

NICE TO HAVE

• Databricks certification or equivalent demonstrated platform expertise
• Experience supporting DoW, federal, Advana, or other enterprise data environments
• Experience with CI/CD, infrastructure as code, automated testing, and source control
• Experience using Unity Catalog for data governance, lineage, and access control
• Experience developing streaming pipelines with Spark Structured Streaming, Kafka, Kinesis, or Pulsar
• Experience with orchestration tools such as Airflow, Dagster, or Argo Workflows
• Experience with Docker, Kubernetes, or other containerization and orchestration technologies
• Experience building cloud-native data platforms in secure, regulated,