Software Engineer III

Work Location:

Toronto, Ontario, Canada

Hours:

37.5

Line of Business:

Technology Solutions

Pay Details:

$125,500 - $154,000 CADThe pay details posted reflect a temporary market premium specific to this role that is reassessed annually.

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate’s skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

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 Summary

As 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 arc…