Director of QA

ABOUT YOU

The Director of QA is a senior technical leadership role responsible for owning and scaling Xsolla's quality engineering function across multiple product lines, teams, and time zones. You will define and drive the strategic vision for quality assurance — from test automation architecture and CI/CD integration to AI-assisted testing practices and performance engineering. You will lead a distributed organization of 30+ QA engineers and leads as part of a broader QA organization of 75+ professionals, ensuring predictable, high-quality releases across every product Xsolla delivers.

This is not a management-only position. You must be deeply technical — capable of reviewing automation code, architecting test frameworks, debugging CI/CD pipelines, and evaluating AI-driven testing approaches hands-on.=

Responsibilities

Strategic Leadership & Ownership

• Define and execute the QA strategy across all Xsolla products, aligning quality objectives with business goals and product roadmaps.
• Own QA deliverables end-to-end — from test planning through release sign-off — and be accountable for outcomes, not just activities.
• Establish and enforce quality gates, defect governance policies, and release readiness criteria across all product teams.
• Drive organizational maturity: evolve the QA function from reactive testing to proactive quality engineering embedded in every stage of the SDLC.
• Represent QA at the executive level — articulate quality risks, tradeoffs, and resource needs to VP/C-level stakeholders with clarity and data.


Technical Excellence

• Architect and oversee enterprise-grade test automation frameworks spanning UI (Playwright), API, database validation (SQL), and performance testing (K6).
• Define and maintain standards for test code quality — including code reviews, PR contributions, reusable libraries, and modular test design.
• Deep understanding of Git workflows — branching strategies, merge request reviews, repository hygiene, and code versioning best practices across multiple repositories.
• Expert-level knowledge of CI/CD pipelines — design, optimize, and troubleshoot automated build, test, and deployment pipelines (GitHub Actions, GitLab CI, Jenkins, or similar).
• Ensure all automation is fully integrated into CI/CD pipelines, with clear pass/fail signals, automated regression gates, and performance benchmarks at every release stage.
• Drive adoption of infrastructure-as-code practices for test environments — containerized test execution, ephemeral environments, and environment parity between staging and production.
• Evaluate, select, and implement QA tooling — including test management platforms (Qase), observability tools, and defect tracking integrations.


AI Integration & Innovation

• Proven track record of using AI in day-to-day QA activities — not aspirational, but demonstrated. You have used AI tools to generate test cases, write automation code, analyze test results, identify flaky tests, or accelerate debugging.
• Champion and operationalize AI-assisted quality engineering across the organization — specifically using Claude AI for code generation, test design, automation scripting, and documentation.
• Define governance frameworks for AI-generated test artifacts — validation, review, and sign-off processes to ensure AI outputs meet quality standards.
• Stay current with AI/ML advancements relevant to testing — intelligent test selection, predictive defect analysis, autonomous test generation, and self-healing automation.
• Establish metrics to measure the impact of AI adoption on QA velocity, coverage, and defect detection rates.


Team Leadership & People Development

• Lead, mentor, and develop a distributed team of 30+ QA professionals — including QA Leads, Senior QA Engineers, and QA Engineers — across multiple time zones (Americas, Europe, Asia-Pacific).
• Build a high-performance culture rooted in ownership, technical excellence, continuous learning, and psychological safety.
• Conduct regular 1:1s, career development conversations, and performance reviews. Identify and grow future QA leaders.
• Design and implement onboarding programs, skill development paths, and technical training curricula — including AI literacy for all QA team members.
• Manage resource allocation and capacity planning across multiple concurrent projects, ensuring predictable and sustainable QA delivery.
• Navigate cross-cultural team dynamics with sensitivity and effectiveness — a truly global team requires a global leadership mindset.


Process Rigor & Quality Governance

• Enforce rigorous QA processes across the organization:  • Test case management: Create, maintain, and execute comprehensive test cases in Qase with full traceability (pass, fail, blocked statuses).
• Bug documentation: Ensure all staging bugs are filed with detailed reproduction steps, expected vs. actual behavior, environment details, screenshots/videos, and severity classification — following established training standards.
• Automation framework documentation: Maintain clear, up-to-date documentation for all automation frameworks, ensuring any engineer can onboard and contribute.
• QA Score: Drive iterative improvements in QA Score metrics — tracking automation coverage, defect escape rate, test execution velocity, and regression stability.
• Define and track QA KPIs and OKRs — present quality dashboards and trend analysis to leadership regularly.
• Conduct root cause analysis on production escapes and implement systemic corrective actions.


Cross-Functional Collaboration

• Partner closely with Engineering, Product, DevOps, and Release Management to embed quality practices into every phase of the development lifecycle.
• Work effectively in Agile/Scrum environments — participate in and drive sprint planning, retrospectives, daily stand-ups, and release ceremonies.
• Act as the primary QA stakeholder for cross-functional initiatives — environment strategy, deployment automation, incident response, and post-mortem processes.
• Build strong relationships with engineering leadership to ensure QA is not a bottleneck but an accelerator.

Common Expectations Across All QA Roles

• Must be technical and able to write code for testing and automation.
• Use Claude AI to assist in code creation, automation, and test design.
• Follow QA processes rigorously: create and execute robust test cases in Qase, maintain clear documentation in automation frameworks, ensure traceability of executed test cases with statuses (pass, fail, blocked), and file Staging Bugs with detailed documentation following training standards.
• Contribute to iterative improvements in QA Score and testing processes.
• Strong communication and collaboration skills, analytical mindset, and commitment to continuous learning.
• Ability to work effectively in Agile/Scrum environments, participating in sprint planning, retrospectives, and daily stand-ups.

Required Qualifications

• 15+ years of experience in QA/Quality Engineering, with at least 6+ years in Senior Manager or Director-level roles.
• Proven experience leading teams of 30+ people distributed across multiple time zones and cultural contexts.
• Hands-on technical proficiency — you can write and review test automation code, not just manage people who do. Proficiency in one or more: Python, JavaScript/TypeScript, Java.
• Deep expertise in test automation frameworks: Playwright, Selenium, Cypress, or equivalent for UI; REST/GraphQL API testing tools; database validation via SQL.
• Performance testing experience: K6, JMeter, Gatling, or Locust — designing, executing, and analyzing load/stress/soak tests.
• Expert knowledge of Git: branching strategies (GitFlow, trunk-based), pull request workflows, code review practices, repository management across multiple repos.
• Expert knowledge of CI/CD: designing and maintaining pipelines (GitHub Actions, GitLab CI, Jenkins, CircleCI), integrating automated tests into build/deploy workflows, debugging pipeline failures.
• Demonstrated use of AI tools in professional QA work — Claude, ChatGPT, Copilot, or similar — for test generation, automation coding, defect analysis, or process optimization. Provide specific examples.
• Strong understanding of Agile/Scrum methodologies and experience operating within and across Agile teams.
• Experience with test management platforms (Qase, TestRail, Zephyr, or similar).
• Excellent communication skills — ability to present complex technical quality topics to non-technical stakeholders, write clear documentation, and influence without authority.
• Bachelor's degree in Computer Science, Engineering, or related field (or equivalent practical experience).

Preferred Qualifications

• Experience in the video game, fintech, or e-commerce/payments industry — understanding the unique quality challenges of high-throughput transaction systems.
• Experience with cloud-native architectures (GCP, AWS, Azure) and testing strategies for microservices, event-driven systems, and distributed architectures.
• Familiarity with containerization and orchestration (Docker, Kubernetes) for test environment management.
• Experience building or managing unified test environments — non-production environment strategies that enable safe, deterministic, and scalable validation.
• Knowledge of security testing fundamentals (OWASP, penetration testing, vulnerability scanning).
• Experience with data pipeline testing and data quality validation.
• Background in establishing QA Centers of Excellence or transforming QA organizations from manual-heavy to automation-first.

What Success Looks Like (First 12 Months)

• 90 days: Complete assessment of current QA processes, tooling, team capabilities, and automation coverage. Establish relationships with all QA leads and key engineering stakeholders. Identify top 3 strategic gaps.
• 180 days: Deliver a QA strategy roadmap aligned with product and engineering priorities. Implement measurable improvements in automation coverage and CI/CD test integration. Launch AI-assisted testing pilot across at least 2 product teams.
• 365 days: Achieve measurable reduction in production defect escape rate. Demonstrate >80% automation coverage on critical regression paths. AI-assisted testing practices adopted across all QA teams. QA Score trending consistently upward. Team engagement and retention metrics at or above company benchmarks.