About the role
Key Responsibilities1. Data Quality Assurance
- Validate scraped data against the source website and client requirements.
- Verify field-level accuracy, completeness, consistency, and data types.
- Identify missing fields, incorrect values, duplicate records, and formatting issues.
- Validate product details, pricing, availability, URLs, identifiers, and other relevant attributes.
- Review sample files before they are marked as QA-passed or submitted to clients.
- Ensure that the output structure, column names, file naming conventions, and data formats match the specified requirements.
- Validate samples against the approved requirements and reference examples.
- Maintain evidence of validation and document any defects identified.
- Validate API requests and responses, including HTTP status codes, response structures, required fields, and error handling.
- Verify JSON, CSV, Excel, and other required output formats.
- Test pagination, filtering, and relevant request parameters wherever applicable.
- Use Postman or similar tools for API validation.
- Report defects with clear descriptions, sample records, screenshots, and reproduction steps.
- Coordinate with developers and TLs/ATLs for defect resolution and retesting.
- Identify recurring quality issues and contribute to Root Cause Analysis (RCA).
- Maintain defect logs and track issues until closure.
- Develop and maintain QA checklists for different projects and platforms.
- Identify opportunities to automate repetitive validation tasks using Python.
- Track quality metrics, including defect rates, first-pass acceptance, rework, and sample rejection trends.
- Recommend improvements to reduce recurring errors and improve delivery reliability.
Required Skills
- Strong analytical and problem-solving skills.
- Experience in manual testing, data validation, or software QA.
- Basic to intermediate Python knowledge for data validation and automation.
- Experience working with JSON, CSV, Excel, and structured datasets.
- Understanding of REST APIs, HTTP methods, status codes, and response validation.
- Familiarity with Postman, Excel, and basic SQL/MongoDB queries.
- Excellent attention to detail and documentation skills.
Preferred Qualifications
- Experience in web scraping, crawling, e-commerce data, or data extraction projects.
- Knowledge of HTML, CSS selectors, XPath, and browser developer tools.
- Experience with automated testing frameworks or data comparison scripts.
- Familiarity with Git and issue-tracking systems.
Key Performance Indicators (KPIs)
- Accuracy and completeness of QA validation.
- Number of defects identified before client submission.
- Reduction in recurring defects and sample rejections.
- Compliance with project-specific QA checklists.
- Timely completion of QA reviews.
- Quality and completeness of defect reports and RCAs.
- Effectiveness of retesting and defect closure.
Expected OutcomeThe QA Engineer will establish an independent quality checkpoint within the R&D workflow, ensuring that samples and deliverables are validated against client requirements before final approval and reducing avoidable rework and quality escalations.