Data Architect

Work Location

Toronto, Ontario, Canada

Hours

37.5

Line of Business

Technology Solutions

Pay Details

$125,500 - $148,000 CAD

This role is eligible for a discretionary variable compensation award that considers business and individual performance.

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

The Opportunity

Global Transaction Banking is building a modern data platform from the ground up — not patching what exists, but designing what comes next. Data products, real-time pipelines, AI-ready foundations, regulatory reporting, and the APIs that make all of it consumable by the rest of the business.

As  Data Architect , you will drive the end-to-end technical architecture for how GTB's data ecosystem is designed, governed, and delivered. You will set the architecture, define the data product and API contracts that surface intelligence to client-facing systems, and provide the technical authority for data platform decisions across the organization.

This is a hands-on architecture role. You will be in the code, in the design reviews, and in the room where data strategy becomes engineering reality.

Mandate:  Define and steward the data platform architecture across GTB — ensuring data is trusted, timely, governed, and a genuine engine for analytics, AI, and business outcomes.

Data Platform Architecture

• Define the GTB Data Platform architecture - ingestion patterns, data product design, structure, conventions, and multi-zone storage strategy.

• Establish architectural standards for batch, micro-batch, and real-time data processing using  Apache Spark ,  Flink , and event-streaming technologies.

• Drive the data modelling frameworks — domain-oriented data products, and the metadata and lineage architecture that makes them trustworthy.

• Drive architectural decisions on platform scalability, cost optimization, performance tuning, and operational resilience.

Data Products & API Architecture

• Define the data product architecture — how data assets are packaged, versioned, published, and consumed across GTB domains.

• Design and govern the data API layer — contracts, versioning, access patterns, and the interface between the data platform and consuming applications.

• Establish event-driven data distribution patterns using  Kafka  or  Confluent , enabling real-time data availability across GTB systems.

• Ensure data APIs meet enterprise security, observability, and reliability standards before they are built.

Governance, Quality & Observability

• Own the data governance architecture — metadata management, data lineage, classification, access control, and policy enforcement frameworks.

• Define data quality standards and the automated monitoring infrastructure that enforces them at pipeline and product level.

• Establish platform observability architecture: pipeline health, data freshness SLAs, anomaly detection, and operational alerting.

• Ensure regulatory reporting requirements are embedded into platform design.

AI & Analytics Foundations

• Define the architectural foundations for AI and ML workloads on the platform — feature stores, model serving patterns, vector storage, and prompt pipeline infrastructure.

• Partner with ML Engineers to ensure the data platform is a reliable, low-latency substrate for  Generative AI  and  LLM -powered capabilities.

• Drive adoption of data and AI capabilities that improve operational efficiency, client outcomes, and business decision-making across GTB.

Engineering Standards & Enablement

• Establish and maintain data engineering standards, reference architectures, and reusable patterns across the Data Pod.

• Lead architectural reviews, spike investigations, and critical platform design decisions.

• Drive adoption of DevSecOps, automated testing, data contract testing, and CI/CD for data pipelines.

• Mentor Staff and Senior Data Engineers; grow architectural thinking across the team.

Enterprise Architecture Engagement

This role operates within the enterprise architecture governance model and is expected to lead and represent the Data domain through all EA touchpoints:

Process & Accountabilities

• Architecture Engagement & Triage  - Register new data platform and AI initiatives; scope architecture risk and complexity; route to appropriate review track.

• Design Council / Peer Review  - Present Data architectural decisions for peer challenge; review and provide input on cross-domain proposals from Channels and Platform.

• Architecture Blueprinting & Platform Vision  - Own and maintain the GTB Data architecture blueprint; align to enterprise data strategy and multi-year platform roadmap.

• Architecture Certificate / APAT Approval  - Obtain architecture approval for all qualifying data platform and AI initiatives; ensure designs meet enterprise standards before build entry.

• Standards & Controls Alignment  - Ensure data platform solutions comply with enterprise data governance, security, privacy, and integration standards; identify and formally manage exceptions.

• Business Architecture & Capability Governance  - Map data and analytics capabilities to business capability model; participate in capability investment planning and data product roadmap governance.

• Delivery Readiness Handshakes (PI / Build Execution)  - Confirm architecture completeness at PI planning gates; support delivery teams through architecture queries during build and validate that solutions are built to design.

What We're Looking For

Core Requirements

• 10+ years  of data engineering or software engineering experience, with the last 3+ in an architecture or technical leadership role.

• Proven track record designing and delivering production-grade data platforms at enterprise scale — not just contributing to them.

• Deep expertise in  Databricks  — platform architecture, Delta Lake, workspace governance, cluster management, and engineering best practices.

• Strong foundation in  Apache Spark  and large-scale distributed data processing; experience with real-time or streaming architectures.

• Solid understanding of modern data architecture: data products, data mesh or domain-oriented design, metadata management, and data lineage.

• Hands-on experience with event-driven architectures and streaming platforms ( Kafka ,  Confluent , or equivalent).

• Experience with  Microsoft Azure  and cloud-native data platform delivery.

• Comfortable with ambiguity — able to define the path forward when requirements are incomplete and tradeoffs are real.

Nice to Have

• Experience with  Apache Flink  or other stream-processing frameworks.

• Familiarity with AI/ML platform patterns: feature stores, model registries, vector databases, LLM integration.

• Exposure to regulatory reporting or data governance in a financial services context.

• Experience with graph or document data models ( Neo4J ,  MongoDB ).

Why Join Us

This is a rare opportunity to do foundational architecture work on a platform that genuinely matters — powering regulatory reporting, client analytics, AI products, and real-time transaction banking capabilities for a major financial institution. You'll have real authority over technical direction, proximity to business outcomes, and a team of strong engineers.

The data problem