Director, Software QA

The Director of Global Quality Assurance is responsible for defining and executing the enterprise QA strategy across a distributed engineering organization spanning North America, Europe, and Vietnam. This role leads both manual and automated testing functions, drives quality engineering transformation, and integrates modern AI-assisted testing approaches to improve coverage, efficiency, and release confidence.

The ideal candidate combines deep technical expertise in test automation frameworks with proven leadership experience managing large, globally distributed QA teams. Experience in warehouse execution systems (WES), warehouse control systems (WCS), or industrial automation environments is highly desirable.

Key Responsibilities

Global QA Strategy & Leadership

• Define and implement a global QA strategy covering functional, integration, performance, and regression testing.
• Lead and scale distributed QA teams across the US, Europe, and Vietnam.
• Establish standardized QA processes, governance models, and KPIs across all regions.
• Drive a shift from traditional QA to Quality Engineering (QE), embedding quality earlier in the SDLC.

Automation & Tooling

• Own the automation strategy, including framework selection, implementation, and scalability.
• Oversee development and maintenance of automation frameworks using tools such as Playwright, Selenium, and API testing frameworks.
• Lead performance and load testing initiatives using tools such as JMeter.
• Ensure CI/CD integration of automated test suites (e.g., GitHub Actions, Jenkins, ArgoCD pipelines).

AI-Driven QA Transformation

• Identify and implement AI/ML-driven testing solutions (e.g., test case generation, self-healing tests, defect prediction).
• Leverage AI tools to improve test coverage, reduce manual effort, and accelerate release cycles.
• Evaluate emerging AI QA platforms and integrate them into the testing ecosystem where appropriate.

Cross-Functional Collaboration

• Partner with Engineering, DevOps, Product, and Architecture teams to align quality objectives with business goals.
• Integrate QA processes into modern DevOps pipelines and Kubernetes/OpenShift-based environments.
• Drive shift-left and shift-right testing practices, including observability and production validation.

Domain-Specific Quality (Preferred)

• Support quality initiatives for WES (Warehouse Execution Systems) and WCS (Warehouse Control Systems) platforms.
• Ensure validation of complex workflows involving automation systems (e.g., robotics, conveyors, AutoStore, Geek+).
• Oversee integration testing across hardware/software boundaries.

Operational Excellence

• Define and track QA metrics (defect leakage, automation coverage, test cycle time, MTTR).
• Implement risk-based testing approaches and quality gates for releases.
• Manage vendor relationships and external QA partners where applicable.

Qualifications

Required

• 10+ years of experience in QA/QE, with at least 5+ years in a leadership role.
• Proven experience managing globally distributed QA teams, including offshore models (Vietnam strongly preferred).
• Strong background in both manual testing and test automation.
• Hands-on experience with automation frameworks (e.g., Playwright, Selenium).
• Experience with performance testing tools such as JMeter.
• Deep understanding of CI/CD pipelines and DevOps practices.

Preferred

• Experience with WES, WCS, supply chain, or industrial automation systems.
• Familiarity with containerized environments (Kubernetes, OpenShift).
• Experience with AI-driven testing tools and strategies.
• Background in messaging systems (e.g., Kafka, AMQ, NATS) and distributed architectures.
• Experience with cloud platforms (AWS, Azure).

Leadership & Soft Skills

• Strong executive presence with the ability to influence cross-functional stakeholders.
• Experience building and mentoring high-performing global teams.
• Excellent communication skills across technical and non-technical audiences.
• Data-driven decision-making mindset with a focus on continuous improvement.

Success Metrics

• Increased automation coverage and reduced manual regression effort.
• Improved defect detection earlier in the SDLC (shift-left effectiveness).
• Reduced production defects and faster release cycles.
• Successful adoption of AI-assisted QA capabilities.
• High team engagement and effective global team collaboration.