Quality Engineering Architect - Tangerine

Scotiabank•Toronto, ON

2d•Onsite

About The Position

The Quality Engineering Architect is responsible for designing, implementing and supporting solutions to testing problems as they relate to project requirements, non-functional testing, UI/UX automation, services, performance testing etc. They are a technical testing expert defining and applying innovative test strategies, technologies and automation approaches to improve the overall efficiency and effectiveness of testing practices at Tangerine with the aim to continually bring in best practices to move “move the needle”.

Requirements

7+ years of demonstrated experience within test automation supporting enterprise wide testing initiatives
5+ years of experience of testing REST / SOAP services
5+ years of demonstrated hands-on experience in various areas of software development, testing framework design and architecture
Well versed in development lifecycle and object oriented design methodologies
Formal software development experience is an asset, particularly for aligning Quality Engineering architecture, automation frameworks and engineering practices
Competent in enterprise application delivery and design techniques
Deep expertise in designing and scaling Quality Engineering architectures, automation frameworks and enterprise testing capabilities
Strong command of cloud-native engineering and platform-aligned testing practices, including containerized execution and orchestration models
Hands-on experience with modern SDLC, source control and CI/CD ecosystems, including GitHub, GitHub Actions, Harness and ArgoCD
Proficiency in modern application development stacks, including Java, J2EE, Spring Boot, JavaScript and Angular, with the ability to align test architecture to engineering patterns
Expertise in API and service validation, including SOAP, REST, service virtualization, mocking techniques and tools such as Postman, Karate and Wiremock
Strong understanding of security, authentication and enterprise integration patterns, including OAuth and PKI
Experience with performance engineering, health monitoring and observability tooling, including Dynatrace and related monitoring practices
Knowledge of data-layer testing across relational and non-relational platforms, including Oracle, SQL Server, MongoDB and Redis
Applied knowledge of AI-enabled Quality Engineering practices, including AI-assisted test design, intelligent automation and model-aware validation strategies

Responsibilities

Provide technical guidance and support to Quality Engineering (QE) resources responsible for the development and maintenance of all technical aspects of QE: UX and UI test automation frameworks both web and mobile e.g. WebDriver.io / Appium o SOAP / RESTful services
Design/implementation of “Accelerators” – custom microservices / apps supporting test o Performance Testing techniques o TestOps – Testing in DevOps world (including Docker, Kubernetes and Google Cloud) and support Cloud Native Development as pertaining to testing
Lead maintenance and support of CICD pipelines and ensure the seamless integration of testing assets into pipelines
Develop application guidelines and standards for Tangerine as pertaining to test automation tooling
Evolve and enhance existing Tangerine testing framework architectures to improve test robustness and effectiveness
Perform analysis of the testing processes, frameworks and tools and provide recommendations for improvements based on best practices, industry standards and business needs
Maintain a technical proficiency with respect to programming languages and processes to ensure that Tangerine is well positioned for technological advances in the marketplace
Work with senior technical personnel and teams (Development, DevOps etc) across IT to find solutions and support testing activities
Advise team members on best practices within testing / automation
Evaluate new technologies and participate in the continuous improvement of applications and systems (e.g. emerging testing frameworks, CICD, cloud native architectures)
Lead the adoption, implementation and support of AI best practices within Quality Engineering, including responsible use, governance, validation, monitoring and continuous improvement of AI-enabled testing capabilities
Establish and promote standards for AI-assisted quality engineering practices, including prompt design, test data protection, model output verification, bias and hallucination detection, security guardrails, traceability and human oversight, aligned to industry risk management and governance practices
Ability work self-governed or within a team; work within an Agile environment