Middle Engineer

BetSymphony is developing a cutting-edge online gambling platform serving multiple brands with a focus on quality, stability, and high performance. The company seeks a Middle QA Engineer to ensure product quality across frontend and backend systems, involved in full product lifecycle and automation efforts.

Location: Ukraine

Responsibilities

• Perform comprehensive manual testing of web and API functionality.
• Design, create, maintain, and execute test cases aligned with sprint commitments.
• Perform regression, sanity, and end-to-end testing.
• Identify, document, and track defects with clear reproduction steps.
• Define testing scope, dependencies, risks, and potential blockers.
• Collaborate closely with developers, product managers, and other stakeholders.
• Contribute to improving QA processes and quality standards across teams.
• Participate in requirement analysis and provide early feedback from a QA perspective.
• Contribute to automation efforts where possible (test scripts, test coverage improvements).

Requirements

• 3+ years of experience in Manual QA (experience in test automation is a strong plus).
• Strong experience in testing web applications, REST APIs, and complex business logic.
• Hands-on experience with writing test documentation: test strategy, test plans, test cases.
• Experience in end-to-end testing of web-based products.
• Understanding of client-server architecture and API testing tools (Postman or similar).
• Preferably basic or intermediate automation skills (e.g., Java + rest-assured, Selenium).
• Experience working with defect tracking tools (Jira or similar).
• Experience working in Agile environments (Scrum / Kanban).
• Strong analytical and problem-solving skills.
• Ability to prioritize testing efforts based on risk and business impact.
• Upper-Intermediate English (written and spoken).
• Strong interest in leveraging AI tools (e.g., ChatGPT, Copilot or similar) to improve testing efficiency.
• Experience or willingness to use AI for generating test cases, exploratory testing ideas, documentation, and bug analysis.
• Ability to use AI to optimize repetitive QA activities and reduce manual effort.
• Critical mindset when working with AI-generated outputs - ability to validate and adapt results.
• Openness to experimenting with AI-driven testing approaches and sharing best practices within the team.