Quality Engineer II

* Partner with Product Owners, Engineering Leads, Architects, and Operations to translate business priorities, NFRs, and architectural changes into risk-based E2E test plans and measurable outcomes.

* Establish governance and reporting for E2E quality: dashboards for coverage, pass rates, flakiness, defect leakage, and release readiness; communicate status and risks to QE leadership.

* Lead integrated defect triage and root-cause analysis across systems using logs, traces, and telemetry, drive remediation and regression prevention through automated checks and improved observability.

* Own E2E test execution planning and delivery for major releases: coordinate dry runs, manage change windows, orchestrate parallel execution, and ensure timely go/no-go recommendations.

* Improve QE processes at scale: standardize frameworks and patterns, introduce service virtualization/mocking where needed, and optimize cycle time through CI/CD integration and environment strategy.

* Act as the primary QE point of contact for stakeholders to facilitate decision-making, resolve blockers, manage expectations, and ensure transparent communication.

* Implement AI solutions to QE Workflow in SDLC.

Where you'll work:

* After completing in-person training and onboarding sessions, you’ll work primarily onsite in Toronto.

* Your remaining time will be spent at a TD location for in-person team events and experiences. The hiring manager will provide more information about how this works for their team.

Job Requirements – What you'll need to succeed:

* Own automation QE delivery for a pod: define automation scope, API/UI coverage, and entry/exit criteria; drive execution to meet sprint and release commitments.

* Partner with Product Owners, Engineers, and stakeholders to translate user stories, NFRs, and architecture into risk-based test strategy and clear, trackable deliverables.

* Partner with the QE from multiple teams during end-to-end integration testing: contribute pod-level test scenarios, execute validation (automation and targeted manual), and support integrated release readiness across dependent systems.

* Leverage architecture diagrams, data flows, and logical data models to build efficient automated test design (API/contract tests, component tests, and UI automation) with maintainable abstractions.

* Build and extend reusable automation frameworks, libraries, and test utilities aligned with QE standards; drive adoption across the pod and contribute back to the automation practice.

* Lead defect triage and root-cause analysis using logs, traces, and test telemetry; partner with engineering to remediate issues and prevent regressions via automated checks.

* Drive QE execution autonomously on complex initiatives, escalating risks early and aligning stakeholders on mitigation plans, quality gates, and release readiness.

* Coordinate with other pods on shared dependencies (services, environments, test data) and provide pod readiness inputs for integrated releases and E2E test cycles.

* Champion modern test approaches (e.g., BDD where applicable, contract testing, service virtualization, data-driven testing) and work with the Automation Practice Lead to scale patterns and governance.

* Continuously improve QE processes and strategy: define quality metrics (coverage, flakiness, defect leakage), streamline test cycles, and introduce automation and CI/CD quality gates.

* Lead and mentor QE engineers within the pod; drive quality discussions in ceremonies (refinement, planning, demos, retrospectives) and ensure shared accountability for quality.

* Integrate automated tests into CI/CD pipelines; manage test execution in build/release workflows and enable fast feedback through parallelization and environment stability improvements.

* Define and evolve test architecture (test pyramid, tooling selection, environment/test data strategy) to support scalable automation and reliable E2E validation.

* Banking domain experience is highly preferred

* Experience implementing AI tools in Quality Engineering is highly preferred.

Required Skills and Experience :

Primary Skills - Rest Assured, Selenium, Postman, BDD, Java, Spring Boot, SQL, GitHub, Azure, Jira, X-Ray, AI tools

BS degree in Computer Science or related Engineering discipline; or equivalent practical experience

8 + years of relevant Quality Engineering / SDET experience with strong hands-on automation delivery

Strong academic background and practical grounding in software engineering fundamentals (testing, CI/CD, and automation best practices)

Strong expertise with AI tools & models to automate Test Case Creation and Automation script generation in QE workflow.