QA Manager

We’re hiring a QA Managerto lead the next evolution of quality engineering across Caseware’s SaaS product teams-driving scalable, modern approaches to testing, automation, and developer enablement. Reporting into the DataAnalytics, Interoperability, and AI Platformorganization, this role is uniquely positioned to define quality strategy across foundational platform areas that underpin our most critical innovations.

What makes this role especially exciting: we’re not just building traditional apps. We’re accelerating delivery of advanced analytics, LLM-based GenAI features, and moving toward agentic AI capabilities-systems that defy conventional testing and require new thinking around test strategy, observability, and statistical validation. You’ll help build teams and architect a quality practice that is not only robust and scalable, but capable of handling the complexity and nuance of modern, non-deterministic systems.

If you’re energized by the challenge of enabling velocity while maintaining trust and quality in data- and AI-driven systems, this is the opportunity to shape that future.

This is a full-time permanent position

This is a exisisting vacancy

Location: This is a hybrid role requiring the successful candidate to work 3 days a week in our Toronto office located at 351 King St E Suite 1100 Toronto ON.

What You’ll Be Doing

• Build and Lead Central Quality Engineering Function

Recruit and lead a team of Software Developers in Test (SDETs) who partner closely with product teams to embed quality into every phase of development-through tooling, automation, infrastructure, reporting and best practices.

• Set and Execute Quality Strategy Across the Platform

Define and implement a scalable quality strategy for a growing SaaS platform-shifting testing left, reducing UI test reliance, and strengthening component and contract coverage. Improve E2E test stability, data setup, and isolation to boost reliability and speed. Enable fast, actionable feedback loops without slowing team velocity.

• Design for Quality in AI & Analytics Systems

Collaborate with data and AI teams to design quality strategies for non-deterministic systems like pipelines, statistical models, and GenAI features. Apply advanced validation methods such as golden datasets, LLM-as-judge, behavioral testing, and synthetic test generation to support continuous delivery.

• Improve CI/CD and Developer Workflows

Optimize CI/CD pipelines for speed and reliability. Define meaningful, maintainable quality gates that provide early warning without blocking delivery. Champion tooling and practices that reduce flaky tests and noisy signals.

• Lead Non-Functional and Production Testing

Establish frameworks and processes for performance, scalability, and resilience testing across our services. Enable testing-in-production approaches through canary releases, synthetic monitoring, and fault injection.

• Manage Platform Release Management

Oversee release management from a quality lens-ensuring readiness through test coverage, risk assessment, and coordination of validation activities across environments.

• Build Test Data and Observability Systems

Drive the creation of scalable, privacy & regulatory compliant test data management strategies. Integrate logs, traces, and monitoring into automated test systems to improve diagnosis and root cause analysis of test failures.

• Define and Measure Quality KPIs

Track key DORA metrics such as test coverage, defect trends, test effectiveness and execution health, flakiness, and regression rates. Use these insights to continually refine the quality strategy and guide investments in automation and infrastructure.

• Drive Culture and Practice Alignment

Partner with engineering and product leaders to instill a quality-first mindset. Educate developers and SDETs on effective test strategies. Participate in incident reviews and technical planning to ensure that lessons learned are translated into durable quality improvements.

What You Will Bring

• • 7+ years' experience leading QA or quality engineering teams within modern SaaS or platform organizations in cloud-native environments (AWS).

  • Expertise in building and scaling automated testing infrastructure, including integration with modern CI/CD pipelines (GitHub Workflows).

  • Knowledge of testing strategies across the pyramid, from unit to E2E, including contract and performance testing (Cypress, Pact, K6).

  • Solid front-end knowledge and testing fluency in HTML/CSS/JavaScript/TypeScript, and back-end knowlege in RESTFul APIs.

  • Experience applying AI tools in the testing lifecycle, such as for test generation, defect prediction, or intelligent automation, to improve coverage and efficiency.

  • Experience testing non-deterministic systemssuch as LLM applications, or statistical algorithms-along with familiarity with validation approaches for GenAI or agentic systems (e.g. snapshot testing, LLM-as-a-judge, synthetic test case generation).

  • Hands-on familiarity with observability tools (New Relic) and best practices for improving test signal and pipeline feedback loops.

  • Experience integrating QA tools like Zephyr Scale and on-prem CI/CD systems (GitHub)

  • A strategic mindset paired with a pragmatic, systems-thinking approach to quality at scale.