QA Engineer - Data Acquisition

Role & responsibilities

• Design and execute test plans for new datasets and data pipelines, including validation of schema, transformations, and data quality checks.
• Investigate data anomalies and complex defects, isolate root causes, and provide clear, structured documentation for development teams.
• Create, run, and maintain SQL (and possibly NoSQL) queries to verify data, test hypotheses, and support defect analysis in production and staging environments.
• Use Jira and Confluence (or similar tools) for defect tracking, test case documentation, and sharing knowledge or runbooks.
• Collaborate with developers and stakeholders to resolve disputes about data vs. code issues, using evidence-based analysis and clear communication.
• Perform repetitive validation tasks on large datasets while maintaining high attention to detail and consistency.
• Work effectively in a multicultural, geographically distributed team and manage overlapping hours with a core team in UTC+2/+3.
• Contribute to a constructive team and company culture through proactive communication and feedback.

Preferred candidate profile

• 36 years of QA experience, preferably in data/ETL or analytics rather than only UI testing.
• Strong SQL skills, able to write complex queries to validate datasets and investigate anomalies.
• Hands-on experience with Jira and Confluence for bug tracking, documentation, and owning complex defects end-to-end.
• Proven experience testing datasets and data pipelines, with good understanding of data quality dimensions.
• Comfortable with repetitive data validation work while maintaining very high attention to detail.
• Effective communicator who can clearly describe complex bugs and handle disagreements using evidence.
• Prior work in multicultural, distributed teams (ideally overlapping with UTC+2/+3) and values transparent, collaborative culture.