Quality Assurance Developer - Dev QA

Key Responsibilities Data Validation & Comparison • Compare Excel output vs JSON output to ensure correctness, completeness, and structural integrity. • Validate schema, key-value pairs, formatting, and business rules. • Normalize and flatten JSON to align with Excel tabular formats. • Write and maintain Python scripts (Pandas/JSON libraries) for automated data comparison. Quality Assurance • Create detailed test plans, test scenarios, and test cases for data validation workflows. • Perform functional testing on services, APIs, and data pipelines that generate outputs. • Identify defects, analyze root causes, and work closely with developers to resolve issues. • Validate regression outputs to prevent data drift across releases. Documentation & Reporting • Document data comparison rules, testing procedures, and validation logic. • Provide clear defect reports with reproducible steps and detailed examples. • Create and maintain QA dashboards, logs, and reports as required. Team Collaboration • Work cross-functionally with Development, Product Engineering teams. • Drive QA standards, best practices, and improvements to validation processes. Required Skills & Qualifications Technical Skills • Strong proficiency in Python (Pandas, JSON parsing, data transformation). • Advanced Excel skills (VLOOKUP/XLOOKUP, pivot tables, conditional formatting). • Experience with JSON, nested data structures, and schema validation. • Familiarity with API testing using tools like Postman or similar. • Experience with data diff tools (VS Code diff, Beyond Compare, WinMerge). • Solid understanding of QA methodologies, functional testing, and defect lifecycle. Analytical Skills • Ability to analyze complex datasets and identify inconsistencies. • Strong problem-solving skills and ability to debug logical errors. • Ability to interpret business rules and apply them to data validation. Bonus Skills • Experience with SQL (joins, filters, data validation). • Knowledge of automation frameworks (PyTest, Robot Framework). • Experience with Jupyter Notebooks for data visualization. • CI/CD pipeline familiarity for automated test execution. • Understanding of cloud-based storage (AWS S3, Azure Blob). Education & Experience • Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field. • 3–7 years of experience in QA, Data QA, Data Validation, or Data Engineering QA roles. • Experience validating outputs from APIs, ETL pipelines, or reporting systems is highly desirable.