Manager, Software Engineering QA at Cricut

Quality & Technical Leadership  • Lead end‑to‑end quality strategy across Desktop Applications, Mobile (iOS/Android), APIs, and Hardware‑connected software.  • Own release readiness, quality risk assessment, and go/no‑go decisions.  • Define clear expectations for feature, API, and system qualification standards.  • Remain hands‑on when needed by reviewing test strategies, test plans, automation results, and complex defect investigations.  • Execute and analyze automation test suites.  • Debug and triage complex issues across frontend, backend, APIs, and system integrations. AI‑Enabled QA Leadership  • Use AI tools in day‑to‑day QA activities to improve efficiency and coverage, including:   • Change log and release notes validation  • Fix analysis to verify defect resolutions and prevent regressions  • Identifying impacted areas across frontend, backend, APIs, and hardware‑integrated workflows  • Test case generation and optimization  • Risk analysis and test prioritization  • Log analysis and defect investigation  • Guide teams on effective use of AI while maintaining strong engineering and testing judgment.  • Help adopt AI‑assisted practices to improve QA quality, consistency, and team maturity. People & Team Leadership  • Manage and mentor QA engineers and QA leads, providing technical guidance, performance feedback, and career growth support.  • Coach engineers on test strategy, risk‑based testing, automation thinking, and quality ownership.  • Balance delivery execution with long‑term team development and scalability.  • Build strong team capability through coaching, standards, and best practices.  • Help scale QA by improving execution consistency, predictability, and maturity. Cross‑Functional & Stakeholder Leadership  • Partner closely with Product Management and Engineering to ensure requirements are testable, measurable, and aligned to customer impact.  • Represent QA in cross‑functional forums, release discussions, and leadership reviews.  • Communicate quality status, risks, dependencies, and trade‑offs clearly to stakeholders and executive leadership.  • Present data‑driven quality metrics and insights to support decision‑making. Process & Continuous Improvement  • Continuously improve QA processes, workflows, and best practices across teams.  • Contribute to defining and evolving quality metrics, dashboards, and operational reporting.  • Drive improvements in test efficiency, automation effectiveness, reliability, and release predictability.