Data QA Engineer

Role Overview

Accountabilities

• Translate customer data extraction and web scraping requirements into clear, testable criteria and comprehensive QA strategies, taking ownership of quality assurance from the beginning of new projects.
• Perform exploratory, manual, and visual testing, complemented by automated testing where it provides measurable value.
• Execute test cycles on extracted datasets to validate compliance with customer specifications and identify inconsistencies, inaccuracies, missing information, and other defects.
• Document defects clearly and thoroughly, providing reproducible steps and supporting evidence so developers can investigate and resolve issues efficiently.
• Apply descriptive statistics, data summaries, and visualizations to assess completeness, accuracy, consistency, and data drift.
• Prepare quality reports and scoring metrics that communicate results honestly to internal teams and, where appropriate, customers.
• Clearly identify the scope of testing, any untested areas, and the associated risks to support informed project decisions.
• Collaborate with project managers and customers to clarify ambiguous requirements and ensure that expectations are measurable and testable before QA begins.
• Contribute to the development and improvement of test automation frameworks, with an emphasis on coverage, reliability, and performance.
• Identify, propose, and implement improvements to QA processes and tooling, including browser extensions, spreadsheet add-ons, and user-interface-based testing tools.
• Work independently while maintaining close collaboration with QA leadership, developers, and project managers to uphold quality standards throughout delivery.

Requirements

• At least 5 years of experience as a QA Engineer or in a comparable role involving software testing, data testing, or data quality assurance.
• Proficiency in Python for data analysis and validation, preferably using Pandas and Jupyter notebooks.
• Basic to intermediate SQL skills, including the ability to query and validate data in at least one relational database environment.
• Strong understanding of web technologies and protocols, including HTML, XPath, JSON, HTTP, and CSS.
• Experience testing HTTP/REST APIs and validating their responses against expected requirements.
• Solid knowledge of test case design, test data management, and practical approaches to exploratory and automated testing.
• Understanding of data quality principles, particularly completeness, accuracy, consistency, and data drift.
• Experience working in an Agile environment, such as Scrum or Kanban, and collaborating effectively with developers.
• Familiarity with AI and large language model (LLM) tools to improve test case generation and accelerate data validation workflows.
• A proactive, detail-oriented mindset with strong ownership of project quality and the confidence to uphold standards under delivery pressure.
• Ability to learn new technologies quickly, understand complex project requirements, and adapt testing strategies to different domains.
• Strong written and spoken English communication skills, with the ability to explain technical findings clearly and precisely.

Nice To Have

• Practical knowledge of Scrapy or other Python-based web scraping frameworks.
• Experience working with test automation frameworks.
• An interest in data science concepts and their application to data analysis and validation.
• Enthusiasm for exploring AI and LLM-powered approaches to improve data quality assurance.

Benefits

• 35 days of paid holidays, supporting substantial time away from work.
• Flexible, location-independent working, with the freedom to work from where you are most productive.
• Global, multicultural team: join a self-motivated, progressive, and internationally distributed team.
• Modern technology: work with cutting-edge open-source technologies and tools.
• Autonomy and ownership: contribute ideas, take responsibility for improvements, and help shape QA processes and tooling.
• Collaborative environment: work closely with technical and project teams to solve meaningful data quality challenges.
• Additional details regarding salary, healthcare, retirement plans, and other benefits were not specified in the original job description.

How Jobgether Works

• Jobgether uses an AI-powered matching process to review applications quickly, objectively, and fairly against the role's core requirements.
• The shortlist is shared directly with the hiring company, whose internal team manages final decisions and next steps such as interviews and assessments.