Data QA Engineer - Remote
在 Zyte,我们吃数据早餐,你可以在任何地方吃你的早餐并为 Zyte 工作。成立于2010年,我们是一个全球分布式的团队由250多名来自28个国家的 Zytans 组成,他们致力于让客户能够提取他们需要的数据来继续创新和发展他们的业务。我们相信所有企业都应该获得流的数据。角色概述数据质量是我们如何确保我们提供的数据正确,客户看到之前。作为数据质量工程师,你将检查我们的蜘蛛和人工智能辅助项目产生的数据与客户要求的相比,找到错误的事情,告诉团队你发现了什么。质量保证比在结束时提出的问题更有用。这个要求是否可测试,我们怎么知道它是错误的?这一角色的一部分就是提早问问题,所以问题在到达客户之前就会得到解决。关键责任 · 将客户的扫描和数据需求变成一个清晰的测试方法,并自从新项目开始拥有 QA. ·将探索手册和视觉测试与自动化测试结合起来,其中自动化为自己支付费用。·运行检验周期对抽取的数据进行清晰报告。·写出缺陷以足够的细节,让开发人员可以无需对话复制和修复它们。· 使用描述性统计数据,总结和图表来显示数据的完整性,准确性,一致性和漂移程度。· 制作测试报告和质量分数,使团队以及客户在需要时得到一个真实的画面。· 说出什么是被测试的,什么不是,以及它带来的风险。· 在 QA 开始之前与项目经理和客户合作清除不清楚或无法测试的要求。· 提供我们测试自动化框架,重点关注覆盖和性能。·建议并拥有对 QA 过程和工具改进的产品,包括浏览器增添,电子表格增添和基于 UI 的测试自行化工具。·与 QA 领导,项目经理和开发人员合作,并进行最小的监督。要求 · 5年以上 QA 工程师或类似角色 - 测试软件或数据。· Python 优先语言-Pandas 和 Jupyter 用于数据分析和验证。· SQL:基本到中级查询,以及至少一个关系数据库及其工具的舒适性。·对网络技术和协议有良好的理解:HTML,Xpath,JSON,HTTP 和 CSS. ·使用 HTTPS/R 测试编写经验。
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At Zyte, we eat data for breakfast and you can eat your breakfast anywhere and work for Zyte. Founded in 2010, we are a globally distributed team of over 250 Zytans working from over 28 countries who are on a mission to enable our customers to extract the data they need to continue to innovate and grow their businesses. We believe that all businesses deserve a smooth pathway to data.
Role Overview
Data QA is how we make sure the data we deliver is right before the customer sees it. As a Data QA Engineer, you will check the data our spiders and AI-assisted projects produce against what the customer asked for, find what is wrong and tell the team exactly what you found.
Quality assurance is more useful as a question asked at the start than a gate at the end. Is this requirement testable and how would we know if it were wrong? Part of this role is asking that question early, so problems are fixed before they reach the customer.
Key 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 our 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.
- Proactive and detail-oriented QA professional with a strong sense of ownership over project quality.
- Fast learner adept at rapidly understanding complex project domain requirements and adapting to new technologies.
- Leveraging AI and LLM tools to optimize test case generation and accelerate data validation processes.
- 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.
Nice to Have
- 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
As a new Zytan, you will:
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
Originally posted on Himalayas