Director, SDET

Leads the organization’s quality engineering strategy, automation architecture, AI adoption, and release-quality practices while mentoring SDET managers and leads. The role requires extensive testing leadership experience and expertise across automation, CI/CD, cloud, distributed systems, and non-functional testing.

About the job

Responsibilities

Quality Strategy & Architecture

Own the end-to-end quality engineering vision, strategy, and roadmap across multiple product lines or business units.
Establish quality gates, release readiness criteria, and CI/CD processes.
Drive a “shift-left, shift-right” quality culture by embedding testability into design reviews and using production observability to close the loop.
Conduct architectural reviews across new and existing products to proactively identify scalability, reliability, and performance risks.
Evaluate and select open-source and commercial tooling, frameworks, and platforms that scale automation across teams.

AI Adoption

Lead the organization’s AI adoption strategy for quality engineering.
Build or integrate AI/ML-based testing tools to improve coverage, productivity, and defect prediction.
Continuously evaluate and leverage AI across Quality Engineering practices.

Stakeholder and People Leadership

Partner with Engineering, Product, DevOps, and Security leadership to align quality goals with business and release objectives.
Provide regular quality updates, metrics, and strategic recommendations to executive leadership.
Define, establish, and monitor quality and productivity metrics, including KPIs and OKRs.
Lead succession planning and mentor SDET Managers and Leads to build the next layer of quality leadership.

Requirements

15+ years of experience in software testing or quality engineering, including 5+ years in a Director or senior engineering leadership role.
Deep expertise in test automation frameworks such as Selenium, Playwright, and RestAssured.
Proficiency in Java, Python, and JavaScript/TypeScript.
Strong understanding of CI/CD pipelines, including Jenkins, GitHub Actions, and Harness CI/CD.
Experience with containerization technologies such as Docker and Kubernetes.
Experience with cloud platforms including AWS, Azure, or Google Cloud.
Hands-on experience building AI/ML-based testing tools that measurably improve coverage and productivity.
Solid grounding in performance testing, security testing, and other non-functional quality attributes.
Experience architecting test strategies for microservices, distributed systems, and mobile platforms.

Nice-to-Haves

Experience presenting AI or quality-transformation initiatives at conferences.
Experience testing high-transaction SaaS platforms, including CRM, payments, automation, email, or fintech products.
Experience building and testing AI products using Agentic AI, including evaluation frameworks.
Contributions to open-source testing frameworks or published thought leadership on Quality Engineering and AI trends.

Key Competencies

Quality vision and strategic execution.
Non-functional testing, including performance, security, and reliability.
Continuous delivery and operational discipline in a fast-paced, rapid-release environment.
Stakeholder management across Engineering, Product, DevOps, and business functions.
Team building and mentorship.
Data-driven reporting of quality and productivity metrics to executive stakeholders.