Vice President - Business, Data Architect - Transaction Banking Product, TD Securities

TD•Toronto, ON

1d•Onsite

About The Position

The primary goal of this role is to define, own, and evolve the business data architecture that enables business strategy, decision‑making, and operational execution. This role ensures that data platforms, data products, and analytical capabilities are intentionally designed, governed, and aligned to measurable business outcomes. This role serves as the architectural bridge between business strategy and technical execution, translating business objectives into target‑state data architectures, domain models, and data products that can be reliably implemented by engineering teams. This role has three primary areas of responsibility: Translating business strategy into target‑state data architecture and data products, Defining and governing data models, domains, and architectural standards, and Enabling trusted analytics, regulatory alignment, and future‑state capabilities (e.g., Data as a Product, AI/ML). The ideal candidate is a senior data architect with deep experience designing enterprise‑scale data ecosystems and the credibility to influence both business and technology leaders. You will collaborate closely with business owners, product owners, data engineering, analytics, and technology architecture to ensure data solutions are scalable, governed, and aligned to long‑term strategic priorities.

Requirements

10+ years of experience in data architecture, data engineering, analytics engineering, or related roles, with a strong architectural focus.
Proven ability to translate complex business strategy into enterprise‑scale data architectures and data models.
Deep understanding of modern data platforms, including cloud data ecosystems, analytical zones, and data product architectures.
Strong experience with data modeling, semantic layers, metadata management, and data governance.
Experience supporting enterprise data transformations, regulatory initiatives, or strategic data investment planning.
Ability to operate in ambiguity, define structure, and influence without direct authority.
Strong communication and storytelling skills, with the ability to explain architectural decisions to executive and non‑technical audiences.

Nice To Haves

Financial services experience preferred, particularly in payments, commercial banking, or transaction banking.

Responsibilities

Translate business strategy into data architecture: Partner with business leaders, business architects, and process architects to understand strategic goals, KPIs, regulatory requirements, and operational challenges, translating them into data domains, architectural patterns, and target‑state designs.
Define data use cases and data products that directly support decision‑making, client experience, risk management, and operational efficiency.
Map business capabilities and value streams to data sources, critical data elements, metrics, and consumption layers.
Act as a trusted advisor to help prioritize data investments based on architectural fit, reuse, and long‑term value.
Define and govern data architecture: Own and evolve target‑state data architecture across platforms (e.g., data lakes, warehouses, streaming, analytic zones).
Define and maintain enterprise and domain‑level data models, including canonical models and semantic layers.
Establish architectural standards for data ingestion, transformation, modeling, and data product design.
Partner with enterprise architecture, cloud, and security teams to ensure alignment with broader technology and risk strategies.
Guide data engineering teams by providing clear architectural intent, patterns, and design guardrails, rather than hands‑on build ownership.
Enable trusted analytics and business outcomes: Ensure data assets are trusted, discoverable, well‑documented, and fit for purpose across analytics, reporting, and downstream applications.
Define architectural approaches for KPIs, metrics, and semantic consistency across lines of business.
Enable advanced analytics, automation, and AI/ML use cases by ensuring architectural readiness and high‑quality foundational data.
Measure and communicate the value of data architecture through adoption, reuse, risk reduction, and business impact.
Data governance, risk, and architectural stewardship: Define and uphold data governance architecture, including data ownership, quality standards, lineage, metadata, and access controls.
Partner with Risk, Compliance, and Legal teams to ensure architectures meet regulatory and client data obligations.
Assess architectural impacts of new data initiatives, platform changes, and bespoke client solutions.
Establish reusable architectural patterns, reference architectures, and best practices across the data ecosystem.