Lead Full Stack Engineer, Data Catalog Services

TD•Toronto, ON

9d•CA$125,500 - CA$154,000•Onsite

About The Position

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.

Requirements

Undergraduate degree, Post Graduate degree or Technical Certificate
Strong academic background (e.g., computer science, engineering)
5-7 years relevant experience
Exceptional problem-solving skills with a proven track record diagnosing complex system failures and designing resilient solutions.
Strong full-stack expertise: modern frontend frameworks and backend service design, including API design, caching, data modeling, and performance tuning.
Hands-on experience integrating APIs (REST, GraphQL) and event-driven patterns; strong understanding of SQL vs NoSQL trade-offs.
Advanced CI/CD and DevOps practices: automated testing, release strategies (blue/green, canary), infrastructure-as-code, and environment management.
Security-first engineering: threat modeling, secure coding, identity/access patterns, secrets management, and vulnerability remediation.
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/trade-offs clearly to engineers, partners, and leadership.

Nice To Haves

Graduate degree nice to have
Core stack: TypeScript + Node.js backend, React frontend is a plus.
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.
Cloud platform experience (Azure/AWS), containerization, and managed services.

Responsibilities

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 internal policies and external regulations.
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/code reviews, and oversee release readiness and operational gating.
Optimize performance and efficiency: lead enterprise-level analysis, drive remediation and continuous improvement, reduce costs (FinOps), and integrate emerging trends and regulatory needs.
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 (RFCs, guilds, 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.
Act as a recognized SME and technical leader: influence architecture, standards, and key engineering decisions across teams beyond the immediate portfolio.
Lead autonomously on complex, high-impact work: navigate ambiguity, define the approach, align stakeholders, and drive execution end to end.
Strengthen platform and delivery excellence: design reusable capabilities, reference architectures, and playbooks that reduce friction and improve consistency across teams.