Work Location:Toronto, Ontario, Canada
Hours:37.5
Line of Business:Technology Solutions
Pay Details:$125,500 - $154,000 CAD
The pay details posted reflect a temporary market premium specific to this role that is reassessed annually.
As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.
Job Description:Role SummaryAs a lead software engineer, you are accountable for shaping and delivering end-to-end solutions for enterprise-grade platforms and complex initiatives. You set technical direction, define and govern architecture, and lead delivery execution for a specific solution area while influencing adjacent domains. You operate confidently across diverse cloud environments, applying deep knowledge of cloud infrastructure, architecture patterns, and engineering practices to design solutions that scale securely and reliably across enterprise platforms. You are hands-on and outcome-driven: designing and building critical path components, raising engineering standards, and scaling team effectiveness through mentorship, patterns, and automation.
You partner closely with Architecture, Security, Infrastructure, and Product to ensure solutions are resilient, secure-by-design, scalable, observable, and compliant with enterprise standards. You also pioneer responsible AI-assisted engineering by establishing guardrails, validations, and governance that enable speed without compromising quality.
This opportunity is with the Enterprise Data Management Office (EDMO), where you will deliver Data Catalog services as a product, driving enterprise-wide data discovery, governance, and self-service access to trusted data assets.
Key Outcomes- An end-to-end technical solution is delivered predictably with strong SDLC discipline, from design to build, test, deploy and operate.
- Architecture decisions are documented, traceable and aligned with enterprise standards; trade-offs are explicit and risk-managed.
- Cloud architecture and infrastructure decisions support scalable, resilient, secure and cost-effective platform growth across diverse deployment environments.
- Engineering quality improves quarter-over-quarter through standards, automation and coaching across security, performance and maintainability.
- Production stability is strong, with high availability, clear SLOs / SLAs, fast incident recovery and actionable observability.
- Teams ship faster with confidence due to reusable patterns, platform capabilities and AI-enabled workflows with robust guardrails.
Customer- Lead end-to-end solution architecture and delivery: define solution options, align cross-functional stakeholders, own execution and ensure production readiness for complex systems.
- Drive hands-on engineering and modernization: deliver critical components, resolve bottlenecks and implement target-state architecture with incremental, de-risked migration strategies.
- Ensure resilient, scalable and efficient platforms: define and validate NFRs, automate delivery, manage dependencies and continuously improve performance, availability and cost efficiency.
- Lead cloud architecture and engineering decisions across diverse cloud environments, selecting scalable infrastructure patterns, deployment models and integration approaches that align with enterprise standards.
AI Workflow Innovation & Governance- Champion AI-assisted development using modern copilots and LLM agents to accelerate delivery while maintaining enterprise-grade standards.
- Design and roll out AI guardrails, including approved use cases, prompt patterns, code provenance expectations and documentation standards.
- Implement validation pipelines for AI-generated code, including linting / formatting, unit and integration tests, SAST / DAST, dependency scanning, secret detection and architectural rules.
- Define human-in-the-loop review requirements, including code review checklists, threat modelling and design reviews for high-risk changes.
- Establish a repeatable AI workflow for the team, including task decomposition, generation, verification, refactoring and documentation, and measure cycle-time impact.
- Promote responsible use across privacy, data handling, IP considerations, auditability and compliance with internal policies and external regulations.
Shareholder- Ensure governance, compliance and risk alignment: adhere to enterprise frameworks, drive approvals and manage policies in line with business priorities and risk appetite.
- Drive quality and operational excellence: enforce shift-left quality, lead design and code reviews, and oversee release readiness and operational gating.
- Optimize performance and efficiency: lead enterprise-level analysis, drive remediation and continuous improvement, reduce costs through FinOps practices and integrate emerging trends and regulatory needs.
Employee / Team- Scale team impact through enablement: establish patterns, reference implementations, documentation and coaching over individual heroics.
- Mentor and elevate engineering excellence: provide hands-on technical guidance, unblock complex issues, lead knowledge sharing through RFCs, guilds and reviews, and drive continuous learning.
- Foster a high-performing, inclusive team: promote quality, innovation and collaboration; communicate risks proactively; support hiring, onboarding and equitable practices.
Breadth & Depth- Acts as a primary subject matter expert across multiple technical areas from design, support and solutions perspectives.
- Works independently and / or autonomously as a senior lead across a diverse range of complex tasks and operational support of solutions.
- Foresees issues and gaps, identifies emerging industry trends and translates them into practical engineering direction.
- Generally reports to a Senior Manager, Engineering.
Qualifications & Technical Requirements- Exceptional problem-solving skills with a proven track record diagnosing complex system failures and designing resilient solutions.
- Strong full-stack expertise: Python, modern frontend frameworks and backend service design, including API design, caching, data modelling and performance tuning.
- Hands-on experience integrating APIs, including REST and GraphQL, and event-driven patterns; strong understanding of SQL versus NoSQL trade-offs.
- Advanced CI/CD and DevOps practices: automated testing, release strategies, infrastructure-as-code and environment management.
- Security-first engineering: threat modelling, secure coding, identity / access patterns, secrets management and vulnerability remediation.
- Expert knowledge of cloud infrastructure and scalable architecture patterns, including cloud-native services, containerization, infrastructure-as-code, resiliency patterns, observability and secure enterprise deployment.
- AI tooling fluency: practical experience with coding copilots / LLM agents and a vision for safe integration into enterprise workflows.
- Strong communication and stakeholder management: articulate constraints and trade-offs clearly to engineers, partners and leadership.
Preferred Qualifications- Core stack: TypeScript + Node.js backend; React frontend experience is a plus.
- Experience with Service Now development is a strong asset
- Experience in data management, data governance, data cataloging or metadata platforms is a plus.
- Hands-on familiarity or integration experience with Collibra data intelligence solutions is a plus.
- pandas experience for data analysis, profiling, transformation or automation use cases is a plus.
- Deep experience designing and operating solutions across Azure, AWS or hybrid cloud environments, including managed services, container platforms and scalable infrastructure patterns.
Experience and / or Education- Undergraduate degree, Post Graduate degree or Technical Certificate.
- Strong academic background, such as computer science or engineering.
- Graduate degree nice to have.
- 5 - 7+ years relevant experience.
