Quality Engineering Manager
Skills
About the Role
You will lead and develop a distributed team of SDETs, defining testing strategies, automation frameworks, and quality practices. You will embed quality throughout the development lifecycle, guide technical approaches, manage concurrent quality initiatives, communicate risks and trade-offs, and track metrics to improve automation, coverage, reliability, and delivery performance.
Requirements
- 10+ years of experience in Software Testing or Quality Engineering
- 3+ years of people management experience leading SDET or QA teams
- Experience leading quality engineering teams within product-based organizations
- Technical background in test automation, quality engineering, and engineering best practices
- Hands-on experience with Java and automation tools such as Selenium or Playwright
- Experience testing distributed systems and microservices-based applications
- Ability to manage multiple projects and competing priorities
- Stakeholder management and cross-functional collaboration skills
- Ability to operate effectively in ambiguous environments and drive outcomes
- Experience with CI/CD, cloud technologies, Docker, Kubernetes, or AWS is a plus
- FinTech, Payments, Banking, or Settlement domain experience is preferred
Responsibilities
- Lead, mentor, and grow a distributed team of SDETs
- Support career development, coaching, performance management, and hiring activities
- Define and evolve testing strategies, automation frameworks, and quality practices
- Partner with Engineering and Product teams to embed quality throughout the development lifecycle
- Improve test coverage, automation effectiveness, reliability, and release confidence
- Review technical approaches and guide automation, testing architecture, and quality risks
- Lead quality initiatives across concurrent projects and workstreams
- Build partnerships across Engineering, Product, Operations, Security, and Platform teams
- Communicate risks, trade-offs, and delivery expectations to stakeholders
- Establish and track quality metrics, test effectiveness, automation coverage, and delivery performance
- Drive adoption of AI-assisted quality engineering and automation
