Job Description
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.
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