Automation Test Lead​/Architect

This role is primarily focused on quality engineering, test automation, API validation, regression testing, test data strategy, and AI-assisted testing. The resource should be comfortable reading code and performing light scripting, but this is not intended to be a traditional application developer role.

The successful candidate will help increase automated test coverage, reduce manual testing effort, improve quality and traceability, and use Kiro and other AI tools to accelerate end-to-end testing activities.

• Support REST API, SOAP/XML, JSON, integration, regression, and end-to-end testing.
• Validate service responses, transformation logic, request/response payloads, and downstream integrations.
• Support test data identification, data conditioning, and reusable test data strategies.
• Integrate automated tests into CI/CD pipelines where appropriate.
• Assist with deployment readiness, smoke testing, regression testing, and production validation planning.
• Reverse engineer existing test assets and calculation validation approaches to support modernization planning.
• Design and implement automated regression testing capabilities for annuity illustration calculations.
• Improve test case generation, execution, comparison, reporting, and traceability.
• Use Kiro and other AI tools to generate test cases, analyze existing logic, create documentation, and accelerate modernization activities.
• Help establish reusable testing patterns, validation frameworks, prompts, and AI-assisted quality engineering assets.

• Test strategy, test planning, regression testing, integration testing, end-to-end testing, defect management, test metrics, traceability, and quality reporting.

• Framework design, automated regression suites, API automation, reusable test utilities, data-driven testing, automated comparisons, and CI/CD test execution.

• REST APIs, SOAP services, JSON, XML, Swagger/OpenAPI, authentication flows, request/response validation, and service-to-service integration testing.

• SQL Server query skills, database validation, test data creation, expected-result comparison, and data quality checks.

• Hands-on use of Kiro, Copilot, Amazon Q, or similar tools for test generation, code analysis, documentation, impact analysis, and quality engineering workflows.

• Ability to read application code, logs, pipeline outputs, API responses, and error messages to isolate issues and support root cause analysis.

• Ability to collaborate with QA, developers, business analysts, architects, actuarial teams, product owners, and vendor resources.