Senior Quality Automation Engineer

Wolters Kluwer•Waltham, MA

About The Position

The Senior Quality Automation Engineer will improve the quality and reliability of the GainsKeeper application by building practical test automation for investment and brokerage workflows. The role works with engineering and product teams and uses AI-powered tools and agents to accelerate test creation, analysis, and maintenance, while reviewing AI-generated output for accuracy, security, and maintainability. The position covers manual and automated testing across UI, API, and integration workflows, including cost basis, tax lots, gains and losses, account and position data, and brokerage integrations. It also contributes to test strategy, requirements analysis, and release validation, and provides release-readiness summaries outlining quality trends, risks, blockers, and recommended actions.

Requirements

Strong understanding of functional, regression, smoke, integration, and exploratory testing fundamentals.
Ability to turn requirements into test scenarios, edge cases, and prioritized coverage using AI-assisted tools.
Experience executing manual tests, documenting results, and reporting reproducible defects.
Familiarity with defect tracking, the defect lifecycle, and Agile development practices.
Understanding of boundary-value analysis, negative testing, and risk-based test prioritization.
Hands-on UI/API automation experience, including programming in Java and TypeScript.
Practical experience with AI-assisted development or agent-based tools, and sound judgment about human review and verification.
Brokerage or financial-services experience and familiarity with investment workflows.
Clear communication and a collaborative approach.

Nice To Haves

Familiarity with the Playwright automation tool.
Experience with tax-lot accounting, cost-basis reporting, or investment tax applications.

Responsibilities

Test cost basis, tax lots, gains and losses, account and position data, and brokerage integrations.
Participate in test strategy discussions, requirements analysis discussions, and release validation.
Use AI-driven tools and agents to draft and maintain tests, analyze failures, identify coverage gaps, and reduce repetitive work.
Review and validate AI-generated output for accuracy, security, and maintainability.
Investigate defects, reduce flaky tests, perform root cause analysis, and document reproducible findings.
Provide release-readiness summaries highlighting quality trends, unresolved risks, blockers, and recommended actions.
Help build and maintain UI, API, and integration tests.
Identify and implement improvements to QA processes and standards independently.