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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