Data Quality Engineer

Trilogy Federal•Arlington, VA

8h•$85,000 - $100,000

About The Position

Trilogy Federal seeks an experienced Data Quality Engineer to support the Department of Veterans Affairs (VA) Summit Advanced Tooling, Lakehouse, and Artificial Intelligence Services (Summit ATLAS) program. This position is contingent upon award, anticipated in early 2027, and requires 1–2 domestic business trips per year. The Data Quality Engineer will develop and implement automated data quality controls to ensure the accuracy, completeness, consistency, and reliability of enterprise data used for analytics, reporting, and artificial intelligence and machine learning (AI/ML) applications. The role supports the Summit Data Platform by validating data ingestion and transformation processes, monitoring data quality, and ensuring datasets meet Government-approved standards before publication and customer use. Responsibilities include designing automated quality tests, reconciling data across source systems and Summit datasets, implementing monitoring and anomaly detection, validating service-level agreements, investigating discrepancies, maintaining quality documentation, and supporting production monitoring and release readiness. The position collaborates with data engineers, data governance specialists, and data owners to apply approved quality rules and publication criteria.

Requirements

Bachelor's degree in Computer Science, Information Technology, Data Analytics, or a related technical discipline, or an equivalent combination of education and relevant professional experience.
Five or more years of professional experience in data quality engineering, data validation, data testing, or a related discipline.
Strong proficiency in Structured Query Language (SQL) for data profiling, reconciliation, validation, and analysis.
Experience developing automated data quality tests and validation scripts using Python or comparable programming languages.
Experience testing Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT) processes in enterprise data environments.
Experience validating data accuracy, completeness, consistency, and integrity across multiple source systems and data platforms.
Experience with data reconciliation, regression testing, anomaly detection, and root cause analysis.
Familiarity with cloud-based data platforms, data warehouses, and lakehouse architectures.
Experience documenting data quality rules, test results, defects, and corrective actions.
Strong analytical, troubleshooting, communication, and collaboration skills.
Ability to meet VA personnel vetting, background investigation, and system access requirements.
Ability to travel for 1–2 domestic business trips per year.

Nice To Haves

Experience supporting VA or other federal agency data modernization initiatives.
Hands-on experience with Microsoft Azure data services, Azure Databricks, or Delta Lake.
Experience implementing enterprise data quality frameworks or tools such as Great Expectations, Soda, or comparable technologies.
Knowledge of Data Management Association (DAMA) data quality principles and enterprise data governance practices.
Experience implementing data observability solutions and automated quality monitoring for large-scale data pipelines.
Experience working with sensitive healthcare data and federal privacy and security requirements.
Relevant certification in data management, data quality, cloud data engineering, or data analytics.

Responsibilities

Design, develop, and maintain automated data quality tests and validation processes for cloud-based data ingestion, transformation, and analytical workloads.
Perform data reconciliation and equivalence testing between source systems, legacy platforms, and Summit datasets to verify data accuracy and migration parity.
Implement data quality checks for completeness, consistency, validity, uniqueness, and timeliness in accordance with Government-approved standards.
Develop and maintain automated monitoring, anomaly detection, and alerting capabilities to identify data quality issues and pipeline failures.
Validate compliance with established data freshness and reliability Service Level Agreements (SLAs), documenting test results, exceptions, and residual risks before dataset publication.
Collaborate with data engineers, data governance specialists, and data owners to implement approved quality rules, thresholds, and publication criteria.
Investigate data discrepancies, perform root cause analysis, and coordinate corrective actions to prevent recurring quality issues.
Maintain data quality rules, test cases, validation results, and supporting documentation in the Data Quality Rule Library and related repositories.
Develop and maintain data quality metrics, dashboards, and reports, including contributions to the Summit Data Quality and Ingestion Dashboard.
Support production monitoring and release readiness by validating corrective actions, retesting affected datasets, and confirming compliance with established quality standards.