Quality Engineer

Skills & Experience

We are looking for Quality Engineers who combine technical capability with strong analytical thinking,

excellent communication and a collaborative engineering mindset.

You should have:

· Excellent communication and collaboration skills, with the ability to explain quality risks, technical

issues and recommendations clearly.

· Strong analytical, investigative and problem-solving skills, with the ability to understand complex

systems and identify patterns and root causes.

· A good understanding of Quality Engineering principles, including automation, risk-based testing,

exploratory testing and defect prevention.

· Experience working within Agile/Scrum engineering teams and collaborating closely with Software

Engineering and Product.

· Practical experience designing, developing and maintaining automated tests.

· A proactive approach to ownership, learning and continuous improvement.

Essential Technical Skills & Experience

The following technical skills and experience are key requirements for the role:

· WebdriverIO (WDIO) or comparable JavaScript/TypeScript-based UI automation frameworks.

· Appium for mobile application automation.

· JavaScript and/or TypeScript automation development.

· Native iOS and Android testing and automation.

· API testing and API automation frameworks.

· Customer Journey / End-to-End automation.

· CI/CD tools and practices, particularly GitHub Actions.

· Test-data creation and reusable test-data approaches.

· Automation reliability, including identifying and resolving flaky or inefficient tests.

· Logs, observability and other engineering tools used for defect investigation and root-cause analysis.

AI & Modern Quality Engineering

Experience or a strong interest in using AI-assisted engineering tools is an important capability for modern

Quality Engineering. We are looking for people who can explore how AI can improve areas such as test

generation, automation development, code and test review, test-data generation, failure analysis, exploratory

testing and engineering productivity.

You should approach AI tools critically and responsibly, validating generated outputs and applying appropriate

engineering, quality and security standards.