Data QA Engineer

Role overview

Accountabilities

• Translate customer data extraction and web-scraping needs into measurable testing strategies from project start.
• Design and execute exploratory, manual, visual, and automated test plans; validate datasets for accuracy, completeness, consistency, and reliability.
• Investigate and document defects with reproduction details; use statistics, summaries, and visualizations to identify anomalies and data drift.
• Prepare test reports and quality scores, communicate testing coverage and limitations, and explain unresolved issues and risks.
• Clarify customer and project requirements so expectations are measurable and testable.
• Improve test automation frameworks, QA processes, and tools, including browser extensions, spreadsheet add-ons, and UI-based testing solutions.
• Work with QA leadership, developers, project managers, and customers to maintain quality standards.

Requirements

• At least 5 years of professional QA experience or comparable quality assurance experience; software testing, data validation, or data quality experience is preferred.
• Strong Python skills; practical Pandas and Jupyter Notebook experience is ideal.
• Basic to intermediate SQL, including queries and work with at least one relational database and its tools.
• Knowledge of HTML, XPath, JSON, HTTP, and CSS; practical HTTP and REST API testing experience.
• Knowledge of test design, planning, execution, test data management, and data quality concepts including data drift.
• Experience with Agile, Scrum, or Kanban and collaboration with software developers.
• Familiarity with AI and LLM tools for test generation, data validation, or QA workflows.
• Strong analytical, problem-solving, communication, adaptability, and quality ownership skills; excellent written and spoken English.

Nice to have

• Working knowledge of Scrapy or other Python-based web-scraping frameworks is a plus.
• Previous experience developing or using test automation frameworks is a plus.
• Interest or experience in data science concepts for analysis, interpretation, and validation is a plus.
• Interest or experience with AI and LLM tools to improve testing efficiency is a plus.

Workplace

• The role is fully remote and the employer describes a flexible working environment.

Benefits

• The posting lists 35 days of paid holidays, an international team, and access to modern open-source technologies.
• Professional growth through challenging data-quality projects and collaboration with experienced colleagues is offered.