Data QA Engineer
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Job Description
Data QA Engineer validating web-extracted customer data for Zyte’s global data extraction platform. Designing tests, analyzing quality metrics, and reporting defects across scraping and AI-assisted projects.
Responsibilities:
- 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.
Requirements:
- 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.
Benefits:
- 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
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