Job Title : Data Quality / QA Engineer
Location : Remote (Ohio, USA)
Employment Type : Contract, Long Term
Prior experience with Cardinal is highly preferred.
Job Overview : We are seeking a highly skilled Data Quality / QA Engineer with strong expertise in C# unit testing , API automation , and Azure DevOps CI / CD pipelines . The ideal candidate will have a background in C# / .NET Core API development and experience designing and maintaining data quality frameworks to ensure the reliability, accuracy, and scalability of enterprise data solutions.
Key Responsibilities :
- Design, develop, and maintain automated testing frameworks for APIs, data pipelines, and integrations.
- Implement unit, integration, and regression tests using xUnit, NUnit, or MSTest frameworks in C# .
- Collaborate with developers and DevOps engineers to integrate testing within Azure DevOps CI / CD pipelines .
- Build and maintain C# / .NET Core API testing scripts and tools for automation.
- Conduct data validation, schema verification, and consistency checks across data models and sources.
- Develop and maintain data modeling and deployment documentation for QA processes.
- Define and report quality metrics to monitor test coverage, defect trends, and release readiness.
- Drive improvements in data quality assurance practices through automation and continuous testing.
Must-Have Skills :
Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.Overall IT Experience : 9+ YearsStrong proficiency in C# and .NET Core API development .Hands-on experience with xUnit, NUnit, or MSTest for automated testing.Expertise in API testing and automation frameworks (Postman, RestAssured, or custom-built).Working knowledge of Azure DevOps CI / CD for test integration and deployment.Experience in data validation, quality matrices , and test documentation .Knowledge of data modeling , ETL validation , and SQL for backend testing.Nice-to-Have Skills :
Experience with Selenium , SpecFlow , or Playwright for end-to-end automation.Familiarity with containerization (Docker) and Kubernetes for QA environments.Exposure to data pipeline testing (Databricks, Azure Data Factory, etc.).Understanding of Agile / Scrum testing methodologies.