Accountabilities:

Quality Strategy and Vision: Own and continuously evolve the company-wide quality engineering roadmap, establishing clear standards, priorities, and accountability frameworks for engineers, Engineering Managers, and Product Managers throughout the software development lifecycle.
Testing Strategy: Define and improve testing approaches across the entire technology stack, including unit, component, contract, API, end-to-end, mobile, and non-functional testing such as performance, load, and accessibility testing.
AI-Native Quality Engineering: Drive the adoption and evolution of AI-powered quality tools, including AI agents and skills that support automated test generation, code reviews, and on-demand quality guidance. Explore emerging AI capabilities and identify opportunities to integrate them into engineering workflows.
CI/CD and Test Infrastructure: Own and improve automated testing infrastructure, quality gates, required checks, end-to-end testing pipelines, visual regression testing, performance budgets, and mobile automation strategies across emulators and real-device cloud environments.
Pipeline Optimization: Determine which quality checks should block deployments and which can run asynchronously, ensuring reliable testing processes without compromising development speed or delivery efficiency.
Production Quality and Monitoring: Connect production monitoring with incident response processes and develop meaningful quality dashboards that engineering teams actively use to track performance and identify issues.
Technical Leadership and Mentorship: Provide technical direction and mentorship to QA engineers across multiple engineering squads, helping them strengthen their testing practices, share knowledge, and adopt consistent standards.
Cross-Functional Collaboration: Partner with Engineering Managers, Product Managers, and engineering teams to embed quality ownership into everyday development practices and ensure quality improvements are treated as planned engineering work.
Continuous Research and Innovation: Evaluate emerging testing methodologies, technologies, and AI capabilities, experimenting with new approaches and determining which innovations should be adopted across the organization.
Technical Enablement: Lead knowledge-sharing initiatives, coaching sessions, and practical training to improve testing maturity and empower engineers to take greater responsibility for software quality.
Quality Performance: Establish measurable improvements in test reliability, pipeline efficiency, automation coverage, and overall engineering quality.
Future Team Leadership: As the QA organization grows, potentially take on direct people management responsibilities, including recruitment, coaching, professional development, and performance management.

Requirements

Professional Experience: Extensive experience in Quality Engineering, Software Testing, and Test Automation, with a proven track record of establishing and improving quality practices across engineering organizations.
AI-Native Mindset: Practical experience using AI tools for quality engineering, particularly automated test generation, AI-assisted code reviews, and intelligent testing workflows. Familiarity with agentic development tools such as Claude Code and Model Context Protocol (MCP)-based workflows is highly valuable. Ability to identify and implement AI capabilities that improve engineering productivity and software quality.
Strategic Quality Leadership: Strong ability to develop quality strategies based on each team's technical maturity and business context. You should be comfortable evolving existing processes rather than imposing standardized frameworks, tools, or arbitrary test coverage targets without understanding team needs.
Hands-On Testing Expertise: Deep practical experience designing, implementing, and maintaining automated testing solutions across the testing pyramid, including component, contract, API, end-to-end, and performance testing.
CI/CD and Infrastructure: Experience building, maintaining, and troubleshooting CI/CD pipelines, automated quality gates, test infrastructure, and deployment validation processes. Ability to resolve pipeline issues and remove technical blockers that affect software delivery.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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