Technology Delivery Lead

Technology Delivery Lead (TDL)

Work Location: Toronto, Ontario, Canada
Hours: 37.5 hrs/week
Line Of Business: Technology Solutions
Pay Details: $96,900 – $136,800 CAD (base + variable compensation award)

Are you a technically fearless engineer‑leader who sees AI not as a buzzword but as a *different way of thinking* — one that reshapes how teams design, build, and deliver software? Do you thrive at the intersection of architecture, data, and human‑centered product delivery?

As Technology Delivery Lead (TDL) embedded in a high‑performing, cross‑functional delivery squad at TD, you will be the technical heartbeat of the team — defining architecture, setting engineering standards, mentoring engineers, and being the first voice in the room when a new feature touches system integration, security, or data — all while championing an AI‑native mindset that continuously pushes the team to work smarter, not just harder.

This is not a purely hands‑off role. You write code, you review PRs, you pair with engineers on hard problems, and you represent the team's technical voice in planning ceremonies and cross‑team dependency conversations.

Job Accountabilities – What You’ll Do

Solution Architecture & Technical Leadership

• Serve as the Solution Architect / Tech Lead (SA/TL) for the delivery squad — the final technical authority for all design decisions within the team
• Design and own end‑to‑end system architecture including API contracts, service/repository layers, data models, and integration patterns between Go backend, React frontend, and PostgreSQL
• Lead architecture reviews at the start of every new feature involving system integration, new components, or external dependencies — before a single line of code is written
• Produce architectural artifacts: sequence diagrams, component diagrams, ADRs (Architecture Decision Records), and API specifications
• Evaluate and articulate trade‑offs between competing architectural approaches, balancing velocity, maintainability, security, and cost
• Define and enforce coding standards, branching strategies, and CI/CD pipeline quality gates across the team

AI‑First Thinking & Innovation

• Champion an AI‑native engineering culture — not just tooling adoption, but a fundamentally different problem‑solving approach: using AI to augment requirements analysis, code generation, test automation, anomaly detection, and decision support
• Leverage large language models and generative AI APIs to build intelligent features — including natural language query interfaces, intelligent summarization, and document understanding pipelines
• Integrate AI‑powered developer tooling into the team's SDLC to accelerate delivery without sacrificing quality
• Prototype and evaluate LLM‑based solutions for business problems; present build‑vs‑buy‑vs‑integrate recommendations to stakeholders
• Continuously scan the AI landscape and bring forward concrete, scoped proposals — not just ideas — for how emerging capabilities can be applied to the product domain
• Coach the team on prompt engineering, RAG patterns, fine‑tuning trade‑offs, and responsible AI use within TD's risk and compliance framework

Google Cloud Platform (GCP) Engineering

• Design and operate cloud‑native workloads on Google Kubernetes Engine (GKE) — including cluster configuration, workload autoscaling, resource quotas, and deployment strategies (blue/green, canary)
• Architect data storage and retrieval solutions using Google Cloud Storage (GCS), including lifecycle policies, IAM bindings, and integration patterns with backend services
• Build and maintain BigQuery data pipelines and reporting views for analytics and executive dashboards
• Leverage Cloud Run, Pub/Sub, Cloud Scheduler, and Secret Manager to build event‑driven, serverless‑adjacent features where appropriate
• Implement observability stacks using Google Cloud Operations Suite (Monitoring, Logging, Trace) — defining SLOs, alerting thresholds, and runbooks
• Manage infrastructure as code using Terraform for GCP resource provisioning and environment parity across dev/staging/prod

Data Architecture & Platform

• Elicit, analyze, and translate business and data requirements into complete solutions — including entity‑relationship models, dimensional data models, ETL/ELT pipelines, and reporting layers
• Design and implement complex ETL/ELT frameworks leveraging Databricks, PySpark, and BigQuery that meet performance, lineage, and governance requirements
• Establish and enforce data quality frameworks, metadata enrichment standards, and data provenance tracking aligned to TD Enterprise Data Governance policies
• Ensure privacy, security, and access control requirements are captured and implemented for all data assets
• Develop and maintain knowledge of upstream data sources, their schemas, and their reliability characteristics; surface risks proactively

Security & Compliance

• Collaborate with the Security / BISO role on any feature involving authentication, authorization, sensitive data, financial data, or external integrations — security review is mandatory before release
• Validate authentication/authorization implementations against TD security standards and OWASP Top 10
• Ensure all AI/ML integrations comply with TD's responsible AI framework and data residency requirements

Agile Delivery & Cross‑Team Collaboration

• Participate actively in SAFe delivery ceremonies — PI Planning, System Demos, Inspect & Adapt — as the technical representative for the team
• Identify and surface cross‑team technical dependencies to the Release Train Engineer (RTE) early; co‑own resolution
• Partner with the Product Owner (PO) before development starts on any feature or epic to ensure technical feasibility, effort accuracy, and risk visibility
• Work with the Business Analyst (BA/BSA) to decompose requirements into technically sound, testable stories with clear acceptance criteria
• Mentor and grow engineers within the team through pairing, code review, design critique, and knowledge‑sharing sessions
• Prioritize and manage own workload to deliver quality results on sprint timelines while unblocking teammates

Where You’ll Work

You’ll be expected to work primarily onsite at a TD location for meetings, team events and experiences. The hiring manager will provide more information about how this works for their team.

Job Requirements – What You Need To Succeed

• Undergraduate degree in Computer Science, Software Engineering, or equivalent technical discipline
• 7+ years of relevant software engineering experience, with at least 3 years in a tech lead or architect capacity
• Demonstrated experience designing and shipping production systems on Google Cloud Platform
• Hands‑on experience integrating LLM/generative AI APIs into production applications

Must‑Have Skills

• Backend Engineering – Go (Golang) – handlers, middleware, REST API design, service/repository patterns
• Frontend Engineering – React + TypeScript, state management, REST API integration
• Data Engineering – SQL, PostgreSQL, data modeling (ERD, dimensional), ETL/ELT design
• Cloud – GCP – GKE, GCS, BigQuery, Cloud Run, IAM, Secret Manager, Terraform
• AI / ML – LLM integration patterns, RAG, prompt engineering, embedding models, generative AI APIs
• Architecture – System design, API contracts, ADRs, sequence/component diagrams
• Security – OWASP Top 10, auth/authz patterns, threat modelling basics
• Agile / SAFe – PI Planning, cross‑team dependency management, story refinement

Strong Assets

• Experience with Databricks and PySpark at enterprise scale/large‑scale transformation
• Experience in financial services or regulated industries (data governance, audit trails, compliance requirements)
• AI developer tooling (e.g. AI‑assisted coding platforms) in a team setting
• Vertex AI – model deployment, MLOps pipelines, model evaluation
• Google Cloud Operations Suite – SLO definition, alerting, distributed tracing
• Experience with human‑centered design (HCD) practices and working in design‑led delivery squads
• Familiarity with OpenShift / Kubernetes in an on‑prem or hybrid context

AI Mindset

• Thinks differently because of AI – approaches problems by asking “what if we didn’t have to code this?” or “what if the system could reason about this itself?”
• Has shipped at least one AI‑powered feature into production and learned hard lessons from it
• Can evaluate AI claims critically – knows when a model is hallucinating, understands latency/cost trade‑offs, and can right‑size AI involvement to the actual problem
• Brings concrete proposals, not abstractions – “here is an AI‑powered feature we could ship in Sprint 3” vs. “we should leverage AI”

Our Total Rewards Package

Base salary, variable compensation, health and well‑being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs.

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