Senior Manager, Software Quality & Release Management

Senior Manager, Software Quality & Release Management Locations: Lakewood, Colorado; Yarmouth, Maine; Billings, Montana; Latham, New York; Lubbock, Texas; Plano, Texas; Pittsburgh, Pennsylvania; Orono, Maine; Cranberry Twp, Pennsylvania. Salary: USD 100000 - 150000 Annually. The Senior Manager of Quality Assurance & Release Management will lead the transformation of software quality practices across Tyler’s School Admin Solutions portfolio — including School ERP Pro, Profund, SISFIN, Absence & Substitutes, and Tyler One integrations. This role is responsible for establishing an AI-first quality engineering organization, where intelligent automation, predictive analytics, and AI-assisted engineering practices are embedded throughout the development lifecycle. Quality in this organization is treated as a technology platform, not a downstream testing function. This leader will leverage AI, automation, and engineering telemetry to build scalable systems that ensure software reliability, accelerate delivery, and improve client outcomes. Reporting directly to the GM of School Admin Solutions, this role maintains independent oversight of product quality while partnering closely with engineering and product leadership to embed AI-enabled quality practices earlier in the development lifecycle. The position oversees QA teams across a global workforce primarily located in the United States and the Philippines. Please note: We are not considering candidates who require visa sponsorship for this position. This is a budgetary decision and due to the significant talent available currently who do not require such sponsorship. Responsibilities Establish an AI-First Quality Engineering Strategy: Define and implement an AI-first QA operating model across all School Admin products. Lead adoption of AI-driven testing approaches, including intelligent test case generation, predictive defect analysis, and automated test optimization. Identify opportunities to apply generative AI and machine learning to improve testing coverage, regression detection, and engineering productivity. Build a Modern Automation Platform: Architect and implement a highly automated QA ecosystem integrated directly into CI/CD pipelines. Ensure automation frameworks leverage AI-assisted capabilities to accelerate coverage and reduce manual testing. Strengthen Release Discipline: Establish predictable release management practices, including release readiness criteria, risk scoring models, and automated validation gates. Implement AI-supported release risk analysis to improve release confidence and transparency. Reduce Regression and Escaped Defects: Implement intelligent regression detection using AI-driven test selection and anomaly detection. Drive root-cause analysis frameworks supported by data and telemetry to prevent recurring defects. Build Quality Telemetry and AI Insights: Implement real-time quality telemetry dashboards tracking defect patterns, automation coverage, regression risk, and release readiness. Use analytics and AI insights to identify emerging quality risks before they impact clients. Drive Shift-Left Quality Practices: Embed automated testing and validation directly into engineering workflows. Ensure AI tools assist developers with code quality checks, test generation, and defect prevention earlier in the lifecycle. Lead and Develop a Global QA Organization: Recruit, mentor, and develop QA leaders and engineers across the U.S. and the Philippines. Build a team culture centered on automation, experimentation, and AI-enabled engineering excellence. Support Strategic Technology Initiatives: Partner with engineering leadership to ensure QA strategy supports cloud modernization, platform transformation, and architectural improvements. Support M&A integrations, ensuring acquired products adopt modern QA standards and automation practices. What Success Looks Like (First 12–18 Months) Establish an AI-first quality engineering operating model across the School Admin portfolio. Implement automation and AI-driven testing that dramatically improves regression coverage and release confidence. Build telemetry dashboards providing real-time visibility into quality trends and release readiness. Reduce regression defects and escaped defects through improved shift-left engineering practices. Develop a strong global QA engineering team across the U.S. and Philippines. Establish QA as a strategic engineering partner that accelerates - not slows - product delivery. Minimum Experience Signals Leadership experience in enterprise SaaS or large-scale software platforms. Experience building or transforming QA organizations, not just operating established processes. Significant experience implementing test automation and CI/CD integrated testing frameworks. Experience applying AI, machine learning, or advanced automation to software engineering workflows. Experience leading distributed or offshore engineering teams. Candidates whose experience is primarily focused on manual QA management or compliance-driven testing organizations may not find strong alignment with this role. Qualifications 7+ years of QA or engineering leadership experience in enterprise SaaS, ERP, or large-scale software platforms. Proven success building QA strategy, automation frameworks, and release governance models. Strong expertise in test automation, CI/CD pipelines, cloud-based testing strategies, and performance validation. Demonstrated experience leveraging AI, machine learning, or advanced automation within software quality processes. Experience building or operating AI-assisted engineering workflows, including AI-generated testing, automated defect detection, or AI-supported code analysis. Experience leading QA teams across distributed or offshore teams. Strong analytical mindset with experience using data and telemetry to guide engineering improvements. Excellent leadership and communication skills with the ability to influence engineering, product, and executive stakeholders. Ideal Candidate Profile The ideal candidate is a builder and innovator who sees QA not as a downstream testing function but as a technology discipline powered by AI, automation, and engineering rigor. This leader should be energized by redefining how quality is achieved in complex SaaS platforms and excited about building a QA organization where AI augments engineers, accelerates automation, and dramatically improves release confidence. #J-18808-Ljbffr