Lead Full Stack Engineer, Data Catalog Services
Explicitly requires vibe coding practices—uses coding copilots/LLM agents with governance, validation pipelines, and human-in-the-loop reviews.
About the Role
Lead Full Stack Engineer responsible for designing and delivering enterprise-grade Data Catalog services at TD, driving architecture, hands-on implementation, and delivery excellence. The role leads AI-assisted engineering governance, ensures resilient secure platforms, and mentors teams to scale engineering quality and operational stability.
Job Description
Role
Lead end-to-end solution architecture and delivery for Data Catalog services within TD’s Enterprise Data Management Office. Set technical direction, define architecture and non-functional requirements, deliver critical components, and scale team effectiveness through mentorship, patterns, and automation.
Key Responsibilities
- Define solution options, align cross-functional stakeholders, and own execution to ensure production readiness for complex systems.
- Design and build resilient, secure, and scalable platforms with clear SLOs/SLAs and actionable observability.
- Drive modernization and migration strategies with incremental, de-risked approaches.
- Improve engineering quality via standards, automation, shift-left practices, and release readiness gating.
- Establish reusable patterns, reference implementations, and platform capabilities to speed delivery.
- Mentor engineers, lead knowledge sharing (RFCs, guilds, reviews), and foster an inclusive, high-performing team.
AI Workflow Innovation & Governance
- Champion AI-assisted development using coding copilots and LLM agents while establishing enterprise guardrails.
- Design AI governance: 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).
- Define human-in-the-loop review requirements for high-risk changes and measure AI workflow impact on cycle time.
Requirements
- Undergraduate degree or technical certificate (graduate degree is a plus) and 5–7 years relevant experience.
- Strong full-stack expertise: modern frontend frameworks and backend service design, API design, caching, data modeling, and performance tuning.
- Hands-on experience integrating REST and GraphQL APIs and using event-driven patterns; solid understanding of SQL vs NoSQL trade-offs.
- Advanced CI/CD and DevOps practices (automated testing, release strategies such as blue/green and canary, infrastructure-as-code, environment management).
- Security-first engineering experience: threat modeling, secure coding, identity/access patterns, secrets management, and vulnerability remediation.
- Practical experience with coding copilots/LLM agents and a vision for safe enterprise integration.
- Strong communication and stakeholder management skills.
Preferred Qualifications
- Core stack experience: TypeScript, Node.js backend, React frontend.
- Experience in data management, data governance, data cataloging, or metadata platforms.
- Familiarity with Collibra data intelligence solutions.
- Cloud platform experience (Azure, AWS), containerization, and managed services.
Outcomes & Impact
- Deliver predictable end-to-end solutions with strong SDLC discipline.
- Documented, traceable architecture decisions aligned with enterprise standards.
- Improved engineering quality and production stability through automation, observability, and operational excellence.
- Scaled team impact via tooling, reference architectures, and coaching.