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# Data Engineer

- **Company:** Trilogy
- **Location:** Arlington, VA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Automation of Tests, Microsoft Azure, Cloud Computing, Cloud Database, Data as a Services, Data Architecture, Data Validation, Information Engineering, Data Files, Data Governance, Data Integration, Extract Transform Load (ETL), Data Warehousing, Distributed Computing Environment, Machine Learning, Meta-Data Management, Performance Tuning, Software Engineering, SQL Databases, Data Streaming, Enterprise Data Management, Data Processing, Enterprise Software Applications, Cloud Platform System, Data Ingestion, Azure Data Factory, Data Lakes, Information Technology, Data Lineage, Apache Kafka, Machine Learning Operations, Azure Synapse Analytics, Software Version Control, Data Pipelines, Legacy Systems, Databricks
- **Published:** November 14, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-arlington-va--d2324677-4b0b-4a73-9ece-ea531578b6b8

## About the Role

Team Player, Financial Management, Government, Healthcare, Artificial Intelligence (AI), Intelligence Agencies, Software Engineering, Department of Veterans Affairs, Machine Tool, Machine Learning, * Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related technical discipline, or an equivalent combination of education and relevant professional experience.

 * Seven or more years of professional experience in data engineering, data integration, or enterprise data platform development.
 * Demonstrated hands-on experience with Azure Data Factory, Azure Databricks, and Apache Kafka.
 * Strong proficiency in Structured Query Language (SQL) and Python for data processing, transformation, and pipeline development.
 * Experience designing and maintaining Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT) pipelines.
 * Experience with cloud-based lakehouse architectures, distributed data processing, and data modeling.
 * Experience implementing automated data quality checks, pipeline monitoring, error handling, and performance optimization.
 * Experience integrating data governance requirements into engineering workflows, including metadata management, data lineage, and policy-based data access controls.
 * Familiarity with source control, automated testing, and Continuous Integration and Continuous Delivery (CI/CD) practices.
 * Strong analytical, troubleshooting, communication, and collaboration skills.
 * Ability to meet VA personnel vetting, background investigation, and system access requirements.
 * The ability to travel for 1-2 domestic business trips per year., * Experience supporting VA or other federal agency data modernization initiatives.
 * Experience working within Microsoft Azure Government cloud environments.
 * Experience with Azure Synapse Analytics, dedicated SQL pools, or comparable enterprise data warehouse technologies.
 * Familiarity with federated query technologies such as Starburst or Trino.
 * Experience migrating complex legacy data warehouse workloads to cloud-based platforms.
 * Experience working with sensitive healthcare data and federal privacy and security requirements.
 * Microsoft Certified: Azure Data Engineer Associate or comparable cloud data engineering certification.

## Description

* Design, develop, and maintain scalable data ingestion and processing pipelines using Azure Data Factory (ADF), Azure Databricks, Apache Kafka, and related cloud technologies.
 * Engineer and optimize data lakehouse solutions, including Delta Lake architectures, semantic models, conformed dimensions, and partitioning strategies that support enterprise analytics and AI/ML workloads.
 * Integrate data from enterprise systems, legacy platforms, and cloud-based sources using batch, streaming, and event-driven ingestion patterns.
 * Implement automated data validation, reconciliation, and monitoring to ensure data accuracy, completeness, consistency, and freshness.
 * Support migration of data pipelines and analytical workloads to Summit, validating parity with source systems and ensuring production readiness.
 * Optimize data processing performance, resource utilization, and cloud costs through workload tuning and efficient data architecture.
 * Collaborate with data governance specialists to ensure ingested datasets are documented in the Enterprise Data Catalog (EDC), including required metadata, data definitions, and end-to-end lineage.
 * Implement Government-approved data governance requirements within engineering solutions, including access controls, data quality standards, and dataset publication criteria.
 * Develop and maintain technical documentation, including pipeline specifications, architecture decisions, operational procedures, and troubleshooting guidance.
 * Monitor production pipelines, investigate failures and performance issues, and implement corrective actions to maintain reliable data services.