Senior QA Automation Engineer
On-siteLondon, England, United Kingdom
Job Summary
Analyze business requirements and database schemas to design, develop, and maintain automated test cases for the ICE Digital Trade Platform. Perform functional, integration, and regression testing on APIs while writing SQL queries for data validation. Leverage AI coding assistants like Claude and GitHub Copilot to accelerate test authoring, failure triage, and defect analysis, reviewing all generated output for quality. Log and track defects, coordinate client-side testing, and respond to API queries during client onboarding. Collaborate with QA leads on complex testing strategies and contribute to internal monitoring tools and CI/CD workflows.
Required Qualifications
- Expert QA experience, including test planning and automation
- Expert experience with JavaScript or TypeScript and Cypress for front-end testing
- Strong knowledge of REST/SOAP APIs, JSON, XML
- Proficient in tools like JIRA, Postman, ReadyAPI, or JMeter
- Experience with SDLC, QA methodologies, and scripting (Python, etc.)
- Excellent communication and organizational skills
- Experience with Git, Jenkins, ALM, and test automation frameworks
- Ability to work in highly demanding, fast-paced environment
- Self-motivated, able to work and excel autonomously on job responsibilities
- Bachelor's degree in Computer Science, Engineering, or related field / similar experience
- Hands-on experience using AI coding assistants such as Claude (including Claude Code) and GitHub Copilot to generate, refactor, and maintain automated test code, with sound judgement on where AI assistance is and is not appropriate
- Working knowledge of agentic AI concepts — autonomous, multi-step AI agents and prompt engineering — applied to QA tasks such as test generation, log and failure analysis, and exploratory testing
Desired Qualifications
- Hands on experience designing and executing performance tests for APIs and GUIs to identify bottlenecks and ensure optimal response times
- Understands and evaluates AI/ML-based testing tools and frameworks, integrating them into CI/CD pipelines to optimise test automation and defect detection
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