Software Quality Engineer

We are looking for an R&D Quality Engineer who will be part of our Retail Software Product Development team. In this role you will focus on supporting the Quality Assurance aspect within a team of test experts. You will have a chance to work with a modern CI/CD process and with the latest technology stack, including Jira, Confluence, Docker, Jenkins, Git, and MS Azure cloud.

You will be supporting the product development of Diebold Nixdorf in the area of Retail Software, specifically around our Vynamic Retail Platform cloud native portfolio.

We seek a hands-on QA with a passion for Quality, a drive to provide customers with Quality product releases and the ability to perform both daily operational tasks (that provide daily quality assurance on build artefacts via manual testing) and forward-looking automated testing of E2E Business Processes (eg. using AI) in a highly agile environment.

Location: Hybrid model is possible (minimum 3 days in Katowice office per week).

• Self-driven person with the ability to turn test scenarios into E2E tests
• Attention to detail and a drive to ensure follow-up
• 3+ years in software quality engineering or test engineering
• Basic SQL skills for test data preparation and result validation
• Hands-on experience with: test management tools (Jira, Xray, or equivalent)
• Detailed understanding of software testing principles and Agile development process
• Ability to write clear, reproducible bug reports and track defect lifecycle end-to-end
• Ability to write technical documentation
• Good English communication skills (speaking and writing) and ability to work in international teams

Nice to have:

• Experience in using AI in the testing process, e.g. Github CoPilot (including concept of MCP’s)
• Elastic stack
• RabbitMQ
• Experience with integrating HW simulation with E2E testing
• Understanding of CI/CD pipelines and their role in automated testing
• Familiarity with Microsoft Azure or other cloud platforms (Kubernetes, AKS, Azure SQL)

Design & Build Tests:

• Create robust, maintainable test suits (UI and integration) aligned with our architecture and quality standards.

Test Data & Environments:

• Support deterministic test data strategies and ensure environment readiness and observability.

Quality Analytics:

• Support tracking quality KPIs (coverage by critical path, defect escape rate) and drive continuous improvements.

Defect Management:

• Triage failures, perform root cause analysis, and collaborate to prevent recurrence via guardrail.

Documentation:

• Maintain living documentation for test strategy, frameworks, environments, and runbooks.

Experimenting with AI Technologies in Test Automation:

• Implementing AI-based solutions for test generation, coverage analysis, anomaly detection, and regression optimization.

Collaboration:

• Collaborate with developers, product owners, and DevOps engineers in an Agile/Scrum environment to ensure quality is built into each sprint.

Reporting:

• Identify, document, and follow up on observations and suggested improvements for future releases.

Retesting:

• Coordinate retest activities after bug fixes and monitor retest progress to closure.