Data QA Engineer

• Turn customer scraping and data requirements into a clear test approach and own QA from the start on new projects.
• Combine exploratory manual and visual testing with automated tests where automation pays for itself.
• Run test cycles on extracted data and report the results clearly.
• Write up defects with enough detail that a developer can reproduce and fix them without a conversation.
• Use descriptive statistics, summaries and charts to show how good the data is: completeness, accuracy, consistency and drift.
• Produce test reports and quality scores that give the team, and the customer where needed, a true picture.
• Say what was tested, what was not, and what risk that leaves.
• Work with project managers and clients to clear up unclear or untestable requirements before QA begins.
• Feed into the test automation framework, with a focus on coverage and performance.
• Suggest and take ownership of improvements to QA process and tooling, including browser add-ons, spreadsheet add-ons and UI-based test automation tools.
• Work with QA leadership, project managers and developers, with minimal supervision.

• 5+ years in a QA Engineer or similar role - testing software or data.
• Python preferred language - Pandas and Jupyter for data analysis and validation.
• SQL: basic to intermediate queries, and comfort with at least one relational database and its tools.
• A solid grasp of web technologies and protocols: HTML, XPath, JSON, HTTP and CSS.
• Experience writing tests against HTTP/REST APIs.
• Good test case design and test data management.
• Experience working in an agile team (Scrum or Kanban) alongside developers.
• An understanding of data quality concepts such as completeness, accuracy, consistency and drift.
• Strong attention to detail, and the confidence to hold the quality line under delivery pressure.
• Clear written and spoken communication in English.
• Working knowledge of Scrapy and other Python-based scraping frameworks.
• Experience with test automation frameworks.
• Interest in data science concepts as they apply to data analysis and validation.
• Interest in using AI and LLM tools to speed up data validation.

• Become part of a self-motivated, progressive, multi-cultural team.
• Have the freedom and flexibility to work from where you do your best work.
• Work with cutting-edge open source technologies and tools.
• 35 days paid holidays