Lead Full Stack Engineer, Data Catalog Services

Work Location: Toronto, Ontario, Canada

Hours: 37.5

Line of Business: Technology Solutions

Salary Range: $125,500 - $154,000 CAD

The 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  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.

As a lead software engineer, you are accountable for shaping and delivering end-to-end solutions for enterprise-grade platforms and complex initiatives. You will set technical direction, define and govern architecture, and lead delivery execution for a specific solution area while influencing adjacent domains. 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 will partner closely with Architecture, Security, Infrastructure, and Product to ensure solutions are resilient, secure‑by‑design, scalable, observable, and compliant with enterprise standards. You will also pioneer responsible AI‑assisted engineering (“vibe coding”) 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.

• An end‑to‑end technical solution is delivered predictably with strong SDLC discipline (design build test deploy operate).
• Architecture decisions are documented, traceable, and aligned with enterprise standards; trade‑offs are explicit and risk‑managed.
• Engineering quality improves quarter‑over‑quarter through standards, automation, and coaching (security, performance, maintainability).
• Production stability is strong: 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.

• 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.

• Champion AI‑assisted development using modern copilots and LLM agents to accelerate delivery while maintaining enterprise‑grade standards.
• Design and roll out AI guardrails: approved use cases, prompt patterns, code provenance expectations, and documentation standards.
• Implement validation pipelines for AI‑generated code: linting/formatting, unit/integration tests, SAST/DAST, dependency scanning, secret detection, and architectural rules.
• Define “human‑in‑the‑loop” review requirements (code review checklists, threat modeling, design reviews) for high‑risk changes.
• Establish a repeatable AI workflow for the team (task decomposition, generation, verification, refactoring, and documentation) and measure cycle‑time impact.
• Promote responsible use: privacy, data handling, IP considerations, auditability, and compliance with…