Data QA Engineer

We are seeking a detail-oriented QA professional with experience in data applications and enterprise data solutions. The ideal candidate will be responsible for validating data accuracy, testing integration workflows, performing reconciliation activities, and ensuring overall data quality across systems. This role requires strong analytical skills, collaboration with cross-functional teams, and experience working in Agile/Scrum environments.

Responsibilities:

• Perform quality assurance activities including functional testing, data validation, regression testing, defect tracking, and issue resolution support.
• Develop and execute test cases, test scenarios, and validation strategies to ensure data accuracy, completeness, consistency, and reliability.
• Validate data models, source-to-target mappings, transformation logic, and business rules across data workflows and applications.
• Conduct data reconciliation and end-to-end validation between source, staging, and target systems to identify discrepancies.
• Create and maintain QA documentation such as test cases, validation results, defect logs, data mappings, and business rules.
• Work with large datasets in CSV, Parquet, JSON, and XML formats for testing, troubleshooting, and defect investigation.
• Collaborate with developers, data engineers, business analysts, and stakeholders to clarify requirements and resolve issues.
• Support release validation and contribute to continuous improvement initiatives within Agile/Scrum teams.

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 3+ years of experience in QA for data applications, system integrations, or enterprise data solutions.
• Hands-on experience with large datasets and formats such as CSV, Parquet, JSON, and XML.
• Strong understanding of data modeling, transformation logic, source-to-target mappings, and data quality validation.
• Experience designing and executing test cases, managing defects, and supporting regression testing.
• Familiarity with Agile/Scrum methodologies and collaborative development environments.
• Strong analytical, troubleshooting, and problem-solving skills.
• Excellent communication and collaboration abilities with technical and business teams.

Note: Flexibility to work in the US time zone is required.