We are the Quality Governance team, consisting of Quality Assurance (QA) and Data Quality (DQ) specialists.
Our mission is to ensure that all services and data assets within the Data Office meet the highest standards of quality and compliance.
We are looking for an intern who is curious, detail-oriented, and eager to contribute to enhancing data reliability and user experience in our analytics products. As a member of our team, you will work closely with QA engineers, data engineers, and frontend/backend developers to ensure — depending on your chosen focus — either the quality of analytics dashboards and custom data visualization reports, or the trustworthiness of underlying data.
Depending on your interests and strengths, you can focus on one of two directions:
Web Application QA: testing interactive dashboards, validating UI behavior, and ensuring that the data displayed to users is accurate and consistent.
Data Quality (DQ): validating datasets, investigating anomalies with SQL, and helping monitor the quality and freshness of data within the Data Office.
This internship offers a unique opportunity to experience how data quality, testing, and observability come together in real-world analytics systems — and to grow into a QA Engineer or Data Quality Engineer role in the future.
Perform manual testing of analytics dashboards and web components
Validate data accuracy between summary cards, graphs, and tables
Test UI elements for consistency, alignment, and responsiveness across browsers and devices
Execute functional and exploratory testing sessions
Document and report bugs with clear steps and visual evidence
Create and maintain test cases and checklists
Collaborate with developers and senior QA engineers to reproduce, investigate, and track issues
Participate in exploratory sessions to identify usability and edge-case problems
Optionally explore UI automation tools such as Playwright
Validate data integrity, completeness, and freshness in analytical datasets
Write and execute SQL queries to compare data sources and detect anomalies
Help monitor and analyze data quality incidents reported by the observability platform
Support QA and Data Engineering teams in investigating metric inconsistencies and tracing root causes
Simple fixes that could be implemented without the involvement of a domain data engineer
Document findings and communicate results clearly in issue trackers
Work with data quality frameworks and observability tools (e.g., Monte Carlo)
Must-Have
Basic understanding of software testing concepts (manual QA, bug reporting, exploratory testing)
Strong attention to detail and a structured, analytical mindset
Curiosity to explore both applications and data to identify inconsistencies
Ability to write clear and reproducible bug reports (YouTrack, Jira, or similar)
Good communication skills in English (written and spoken)
Motivation to learn and contribute to the quality of data and web applications
Depending on your interests, you may lean toward:
◆ Web Application QA focus
Familiarity with web applications and browser-based testing
Ability to validate UI consistency and cross-browser behavior
Basic understanding of HTML/CSS and frontend logic
Optional: experience with QA tools (Chrome DevTools, Postman, TestRail) and interest in UI automation
◆ Data Quality (DQ) focus
Interest in data validation and analytical problem-solving
Knowledge of SQL
Logical thinking and ability to detect data anomalies
Optional: familiarity with analytics platforms (Tableau, Power BI, Looker) or data observability tools (Monte Carlo)
Nice-to-Have
Understanding of the software development lifecycle (Agile, Scrum)
Interest in