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+ Years
• Strong 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.