Data Quality Engineer
Haystack · United States · 2 wk ago
RemoteRemoteQuality AssuranceFull-time
About the role
A leading organization dedicated to supporting veterans by improving healthcare services is seeking a talented professional to join their team. This organization plays a crucial role in enhancing the Community Care Network (CCN) Next Generation Systems Integration efforts, focusing on critical data integrity and analytical capabilities.
Responsibilities
- Establish and maintain data quality, reconciliation, profiling, and analytical capabilities for CCN Next Gen.
- Ensure data accuracy, completeness, consistency, traceability, and fitness for purpose across applications and interfaces.
- Apply data engineering, statistical analysis, automation, and AI-assisted techniques to identify data anomalies and validate transformations.
- Collaborate with cross-functional teams including Synthetic Data Engineers, Healthcare Interoperability Engineers, and business analysts.
- Support end-to-end integration within the VA ecosystem and expand to external TPA and partner integrations.
- Design and implement reusable data quality frameworks and automate data quality controls for various data characteristics.
Requirements
- Proven experience in Data Quality Engineering and/or Data Science roles.
- Strong understanding of data reconciliation methods across complex integration workflows.
- Expertise in data profiling and analysis, especially with structured and semi-structured healthcare data.
- Ability to apply statistical techniques and potentially machine learning for anomaly detection and quality analysis.
- Proficiency in Python, SQL, or comparable languages for data manipulation and automation.
- Experience integrating data quality checks into CI/CD pipelines and supporting dashboards/metrics for visibility.
Benefits
- Opportunity to contribute to meaningful work supporting veterans' healthcare.
- Role within a multidisciplinary Systems Integration Team.
- Chance to work with cutting-edge techniques like AI-assisted analysis.
- A focus on establishing comprehensive, automated data quality and reconciliation capabilities.