Lead Quality Engineer

We are seeking a hands‑on Lead Quality Engineer to help drive the next evolution of Quality Engineering, test automation, and AI‑assisted testing practices across a rapidly growing technology organization.

This role is ideal for a quality‑focused engineering leader who combines deep automation expertise with a modern QE mindset and experience leveraging AI to improve testing speed, coverage, reliability, and maintainability.

The Lead Quality Engineer will partner closely with Engineering, Product, Dev Ops, QA, UAT, TPMs, Data, Security, and Production Support teams to embed quality throughout the software development lifecycle. This individual will design scalable automation frameworks, establish testing best practices, improve release confidence, and help engineering teams deliver high‑quality software faster and more consistently.

You will play a critical role in shaping quality standards across enterprise applications, APIs, integrations, data platforms, and business‑critical technology solutions.

• Influence quality engineering practices across mission‑critical platforms and applications.
• Define automation strategy, test data approaches, quality metrics, and AI‑assisted testing workflows.
• Establish scalable quality standards that improve reliability without slowing development velocity.
• Help teams adopt modern engineering‑driven testing practices and shift‑left quality principles.

This role will leverage AI tools to accelerate and improve:

• Automated test creation and maintenance
• Test case generation and optimization
• Regression analysis and coverage expansion
• Failure analysis and defect investigation
• Quality reporting and insights

The ideal candidate will help establish practical guidelines for AI‑generated testing solutions, ensuring accuracy, security, maintainability, and alignment with business requirements.

• Drive quality engineering practices throughout the full software development lifecycle, from requirements and design through production feedback loops.
• Partner with Product and Engineering teams to define acceptance criteria, testing expectations, integration risks, and release readiness requirements.
• Participate in architecture discussions, backlog refinement, sprint planning, release planning, and post‑release quality reviews.
• Mentor QA engineers and influence engineering teams on automation‑first, risk‑based testing approaches.

• Own and evolve test strategies across UI, API, integration, end‑to‑end, regression, and system testing.
• Develop scalable approaches for test case management, traceability, and coverage reporting.
• Build sustainable regression strategies for critical workflows, APIs, integrations, data dependencies, and enterprise applications.
• Improve visibility into quality risks, test coverage, and release readiness across teams.
• Design reliable approaches for test data creation, management, and environment readiness.
• Develop reusable patterns for synthetic data, test accounts, controlled datasets, and automated testing scenarios.
• Partner with Engineering, Data, Dev Ops, and Security teams to improve environment stability and testing reliability.
• Promote secure, compliant, and repeatable test data practices.

• Own and enhance automation frameworks supporting UI, API, integration, and end‑to‑end testing.
• Maintain and expand Playwright‑based automation capabilities.
• Integrate automated testing into Git Hub‑based CI/CD pipelines to provide fast and reliable feedback.
• Improve automation coverage, stability, execution speed, reporting, and maintainability.
• Reduce flaky tests and establish reusable automation standards across engineering teams.

• Utilize AI‑assisted tools to improve test creation, automation maintenance, test data generation, and defect analysis.
• Develop practical AI‑enabled workflows for test design, regression analysis, automation improvements, and quality reporting.
• Evaluate emerging AI testing capabilities and recommend adoption strategies based on business value and reliability.
• Revie…