QA Manager

Caseware•Toronto, ON

21d•CA$110,000 - CA$125,000•Hybrid

About The Position

Caseware is seeking a QA Manager to lead the evolution of quality engineering across its SaaS product teams. This role will drive modern approaches to testing, automation, and developer enablement, reporting into the Data Analytics, Interoperability, and AI Platform organization. The position is responsible for defining quality strategy across foundational platform areas that support critical innovations, including advanced analytics, LLM-based GenAI features, and agentic AI capabilities. The QA Manager will help build teams and architect a quality practice capable of handling complex, non-deterministic systems, enabling velocity while maintaining trust and quality in data- and AI-driven systems.

Requirements

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 systems such 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.

Responsibilities

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.