Principal Technology Architect - QA

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Precisely is the leader in data integrity. We empower businesses to make more confident decisions based on trusted data through a unique combination of software, data enrichment products and strategic services. What does this mean to you? For starters, it means joining a company focused on delivering outstanding innovation and support that helps customers increase revenue, lower costs and reduce risk. In fact, Precisely powers better decisions for more than 12,000 global organizations, including 95 of the Fortune 100. Precisely's 2500 employees are unified by four company core values that are central to who we are and how we operate: Openness, Determination, Individuality, and Collaboration. We are committed to career development for our employees and offer opportunities for growth, learning and building community. With a "work from anywhere" culture, we celebrate diversity in a distributed environment with a presence in 30 countries as well as 20 offices in over 5 continents.

Precisely is an AI-first organization. All employees are expected to demonstrate proficiency in applying AI tools to accelerate their work, improve output quality, and eliminate low-value tasks. Candidates should be comfortable using generative AI tools (e.g., Microsoft Copilot, ChatGPT) in their day-to-day workflows, able to evaluate AI-generated outputs critically, and open to continuously adopting new AI capabilities as they emerge.

Overview: We are building the next generation of our SaaS platform, and we need someone to own the quality architecture that makes it all trustworthy. As a Principal Technology Architect - QA, you will design the systems, standards, and governance that raise the bar for quality across every product segment we ship. You will put that into practice by bringing AI-driven testing, intelligent automation, and modern quality tooling into the way our teams work every day. This is a hands-on architecture role, not a policy desk. You will build test frameworks, design quality gates, set reliability targets, and create the shared infrastructure that teams actually adopt because it makes their lives easier. You will work closely with engineering leadership to connect quality strategy to business outcomes. When you recommend a change, people across the org will listen, because your technical credibility and track record back it up. You will also mentor engineers, shape how we hire for quality mindset, and help grow the next generation of technical leaders on our teams. If you get excited about solving hard problems at scale, rethinking how quality works in a modern SaaS org, and leaving things better than you found them, we would love to talk.

What you will do:

• Set the quality architecture direction for the org: design quality gates, define reliability targets, and choose the testing infrastructure that all product segments build on.
• Build and maintain a governance model for quality practices so that standards, processes, and tooling are adopted consistently across engineering teams.
• Design and deliver scalable test automation frameworks that teams can adopt and extend to improve coverage, speed, and confidence in every release.
• Bring AI into quality engineering: evaluate and roll out AI-driven test generation, intelligent test selection, defect prediction, and automated monitoring.
• Own the performance and resilience strategy: set performance budgets, define monitoring standards, and create disaster recovery and incident response frameworks.
• Partner with product and engineering leaders to balance release velocity with system health, making sure teams can ship fast without cutting corners on quality.
• Evaluate and introduce new tools, patterns, and practices that keep our quality approach current as the platform and industry evolve.
• Mentor engineers across teams, raise the bar on how we hire for quality mindset, and contribute to engineering maturity across the organization.
• Represent Precisely externally as a thought leader in quality engineering through talks, writing, or industry engagement.

What we are looking for:

• Required:
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Equivalent work experience is accepted in place of a degree. An advanced degree is a plus.
• Experience: 15+ years of progressive experience in software quality engineering, test architecture, or related technical leadership roles, with demonstrated org-wide architecture leadership.
• Quality architecture: Deep expertise in test automation frameworks, CI/CD pipeline design, quality gate implementation, and testing strategies for large-scale SaaS platforms.
• AI: Hands-on experience applying AI to quality workflows (e.g. AI-driven test generation, intelligent test selection, or automated defect detection).
• Leadership: Track record of driving adoption of technical standards across multiple teams or product areas, including working with leaders who do not report to you.
• Travel is required: Yes – Approximately 10%.
• AI Skills/Knowledge:
• Comfortable using generative AI tools (e.g. GitHub Copilot, Claude Code, or custom LLM integrations) to accelerate day-to-day quality workflows such as test generation, defect triage, and root-cause analysis.
• Able to critically evaluate AI-generated outputs and set guardrails so that AI-produced test artifacts meet reliability, security, and compliance standards.
• Experience evaluating and integrating AI-powered quality tools (e.g. autonomous testing platforms, AI-based test maintenance, predictive analytics for defect detection) into org-wide workflows.
• Open to continuously adopting new AI capabilities as they emerge and excited to shape how the org integrates them into quality practices.
• Preferred Skills:
• Experience defining and scaling quality practices across multiple product segments in a large SaaS or cloud-native organization.
• Familiarity with contract testing, chaos engineering, or advanced reliability practices (SLOs/SLIs, error budgets).
• Bonus points for experience establishing quality centers of excellence or community-of-practice models across engineering organizations.
• Exposure to security testing practices, compliance frameworks, or regulated industry quality standards.
• Experience with observability and monitoring platforms (Datadog, Splunk, Grafana, PagerDuty) at an architectural level.
• Track record of representing an organization externally through conference talks, published writing, or industry working groups in quality engineering.

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