Software Engineer In Test
Summary
Validates data-driven healthcare analytics products for population health, payer analytics, and value-based care by building and running automated and manual tests for data pipelines, BI reports, dashboards, and database applications using Snowflake, SQL, Azure Data Factory, and SSIS.
The Software Engineer in Test (SEIT) plays a key role in validating data-driven products that support population health, payer analytics, and value-based care initiatives. This individual will translate functional and non-functional requirements into reliable, maintainable test solutions across reports, dashboards, data extracts, and automated ETL workflows. The ideal candidate brings experience testing healthcare data platforms with a strong understanding of claims, quality, risk adjustment, utilization, and analytics used by payer and population health organizations.
Responsibilities:
- Support testing and quality validation of data and analytics products used across population health, payer, and value-based care environments.
- Design, develop, and maintain automated test frameworks for data pipelines, BI reporting, dashboards, and database-driven applications.
- Validate data movement and transformation processes across Snowflake, SQL, Azure Data Factory, SSIS, and related cloud/data warehouse technologies.
- Create and execute automated and manual test plans to verify data accuracy, integrity, completeness, and performance across claims, quality, risk, and utilization data sets.
- Partner with product, engineering, QA, and business stakeholders to define test scenarios and ensure solutions meet business and operational requirements.
- Review and test reports, dashboards, and data extracts used to support payer operations, care management, quality reporting, and performance measurement.
- Perform root-cause analysis and troubleshoot data quality issues across internal and external healthcare data sources.
- Contribute to release readiness by preparing test artifacts, validating release candidates, and supporting deployments across test and production environments.
- Ensure data validation processes align with enterprise standards for security, recoverability, and reliability.
- Work in Agile delivery models including Scrum, Kanban, or hybrid teams to support iterative development and continuous improvement.