Sr. Quality Engineer

What You’ll Do

• Work in an Agile SDLC to deliver incremental customer and business value while advancing product quality within a scrum team.
• Partner with Product Owners, Software Engineers, Architects, and other QA engineers to clarify requirements, identify ambiguity, and shape testable user stories.
• Own and influence test strategy across manual testing, automated testing, API validation, database validation, exploratory testing, regression coverage, and release readiness.
• Use AI-assisted tools where they improve test planning, automation, debugging, defect analysis, documentation, or technical discovery.
• Design and maintain automated tests using frameworks such as Cypress, Selenium, TestNG, Cucumber/Gherkin, or similar tools.
• Build and maintain API automation that validates service behavior, integration points, data contracts, permissions, and business-critical workflows.
• Use SQL to query, validate, and troubleshoot application data.
• Work with CI/CD tools and deployment pipelines, including GitHub or GitLab, to validate builds, test execution, environments, release quality, and pipeline failures.
• Troubleshoot internal bugs, failed automated tests, customer-reported issues, data inconsistencies, and environment problems with speed and care.
• Identify gaps in testability, observability, automation coverage, requirements clarity, and team process, then help close those gaps.
• Advocate for quality early in the SDLC instead of treating testing as a final checkpoint.
• Collaborate with product, design, support, engineering, and business stakeholders to translate customer needs into validated, reliable product outcomes.
• Mentor junior engineers on mature QA practices, test automation craftsmanship, and risk-based testing.
• Participate actively in stand-ups, retrospectives, backlog refinement, test strategy discussions, and technical design reviews.
• Continuously evaluate tools, technologies, and workflows that can improve engineering productivity and product quality.

What We’re Looking For

Must Have

• 5+ years of hands-on Quality Assurance or Quality Engineering experience.
• Strong test strategy, exploratory testing, regression planning, release readiness, defect investigation, and risk-based validation skills.
• Experience building or maintaining automated tests using at least one modern test automation framework.
• Experience validating APIs, integrations, application data, and business-critical workflows.
• Ability to use SQL to investigate data issues, validate expected behavior, and support defect analysis.
• Working familiarity with CI/CD pipelines, source control workflows, and deployment validation.
• Practical use of AI-assisted tools in engineering or QA workflows, with the judgment to evaluate and improve AI-generated output.
• Excellent problem-solving skills and the ability to break down ambiguous quality risks into clear, executable validation steps.
• Strong communication skills, including the ability to explain quality risks, test strategy, defects, tradeoffs, and release recommendations to technical and non-technical stakeholders.
• A collaborative mindset and demonstrated ability to mentor, guide, and raise the quality bar for other engineers.

Nice to Have

• Node.js, TypeScript, JavaScript, or similar application development experience.
• MySQL, PostgreSQL or similar relational database experience.
• Experience with Cypress, Selenium, TestNG, Cucumber/Gherkin, or similar tools.
• Experience with GitHub, GitLab, Jira, Confluence, or similar engineering collaboration systems.
• Experience testing SaaS products serving industry-specific operational workflows.
• Experience testing point of sale, reservations, bookings, payments, customer management, rentals, contracts, reporting, or marketplace-style systems.
• Experience modernizing legacy test suites or incrementally improving automation coverage in older codebases.

How We Expect Engineers to Use AI

We are especially interested in quality engineers who treat AI as a serious engineering tool and can point to practical ways it has improved how they validate, automate, and ship software. You should be comfortable using tools such as Codex, Claude Code, Cursor, GitHub Copilot, ChatGPT, or similar AI-assisted development platforms to explore product behavior and risk areas, draft test cases/automation code/data queries/documentation, identify edge cases/failure modes/regression risks, debug failed tests/CI/CD issues/logs/API responses, improve test automation, build scripts/prompts/playbooks, and pressure-test assumptions and release readiness.

We value quality engineers who know when AI can accelerate the work, and when careful human judgment, hands-on testing, and direct system validation are required.