Data QA Engineer

Accountabilities

• Translate customer data extraction and web scraping requirements into clear, measurable testing strategies, taking ownership of quality assurance from the beginning of new projects. 
• Design and execute comprehensive test plans combining exploratory, manual, visual, and automated testing approaches according to project needs. 
• Perform regular testing cycles on extracted datasets to validate data accuracy, completeness, consistency, and reliability. 
• Identify, investigate, document, and report defects with clear reproduction steps and sufficient technical detail to enable developers to resolve issues efficiently. 
• Apply descriptive statistics, data summaries, and visualizations to assess data quality, identify anomalies, and monitor potential data drift. 
• Prepare detailed test reports and quality scores, providing internal stakeholders and customers with an accurate understanding of project quality and potential risks. 
• Clearly communicate testing coverage, limitations, unresolved issues, and remaining risks throughout the delivery process. 
• Collaborate with project managers and customers to clarify requirements, identify ambiguities, and ensure that expectations are measurable and testable before QA activities begin. 
• Contribute to the development and continuous improvement of test automation frameworks, focusing on test coverage, reliability, and performance. 
• Identify and implement improvements to QA processes and tools, including browser extensions, spreadsheet add-ons, and user-interface-based testing solutions. 
• Work closely with QA leadership, developers, and project managers to maintain high-quality standards while operating with a high degree of independence. 

Requirements

• At least 5 years of professional experience as a QA Engineer or in a comparable quality assurance role, preferably involving software testing, data validation, or data quality. 
• Strong Python programming skills, ideally with practical experience using Pandas and Jupyter Notebook for data analysis, processing, and validation. 
• Basic to intermediate SQL knowledge, with the ability to write queries and work confidently with at least one relational database and its associated tools. 
• Solid understanding of web technologies and protocols, including HTML, XPath, JSON, HTTP, and CSS. 
• Practical experience designing and executing tests for HTTP and REST APIs. 
• Strong knowledge of test case design, test planning, test execution, and test data management. 
• Understanding of key data quality concepts, including completeness, accuracy, consistency, reliability, and data drift. 
• Experience working in Agile development environments, including Scrum or Kanban, and collaborating closely with software developers. 
• Familiarity with AI and Large Language Model (LLM) tools, particularly their application in generating test cases, accelerating data validation, and improving QA workflows. 
• Excellent analytical and problem-solving abilities, combined with strong attention to detail and a proactive approach to quality ownership. 
• Ability to quickly understand complex project requirements, learn new technologies, and adapt to changing priorities. 
• Confidence in maintaining high-quality standards and raising concerns when delivery timelines or project pressures could compromise quality. 
• Excellent written and verbal communication skills in English.