Senior Director, Data Architecture & AI

RFA•Toronto, ON

2d•Hybrid

About The Position

The Senior Director, Data Architecture & AI is a senior leadership role responsible for defining and executing the Bank’s Data and AI strategy. This role partners closely with business and technology leaders to build a modern, data‑driven, and engineering‑focused capability that enables advanced analytics and AI solutions. These solutions support personalized customer experiences, strengthen decision‑making, and improve operational efficiency across the Bank. The Senior Director leads a team accountable for the operational reliability, governance, and full lifecycle management of the Bank’s data, AI, and Large Language Model (LLM) platforms. The role drives enterprise‑wide adoption of data and AI practices to enhance internal productivity and transform core business processes. As a key contributor to the Bank’s broader AI strategy, this leader provides thought leadership in data architecture, infrastructure, and technology enablement. Success in this role requires a strong blend of technical depth, strategic vision, and the ability to influence and align cross‑functional teams in delivering secure, scalable, and modern data and AI‑enabled financial services platforms.

Requirements

Bachelor’s degree in information systems, Computer Science, Data Management, or related field
7–10 years of progressive experience in Data Strategy, Data Architecture design, BI, Analytics, or Data Management
Hands‑on understanding of analytics, cloud data platforms, and data governance
Experience in evaluating and implementing AI tools, platforms in regulated financial institutes
3+ years in a people leadership or senior individual contributor role
Strong experience with: Data governance frameworks and operating models
Strong experience with: Enterprise BI platforms (e.g., Power BI, Tableau, or similar)
Strong experience with: Data warehousing and reporting architectures
Strong experience with: Data quality management and controls

Nice To Haves

Experience in financial services or regulated environments is strongly preferred

Responsibilities

Responsible for defining and governing the enterprise-wide data strategy to support business growth, digital transformation, analytics, AI, and regulatory requirements.
Define and own the enterprise data architecture implementation vision, principles, capability model, and roadmap.
Lead current-state assessments of data architecture, platforms, governance, and operating models to identify gaps, risks, and transformation opportunities.
Provide clear architectural direction that balances innovation, resilience, security, and cost efficiency across the enterprise.
Act as a trusted authority on how data architecture enables revenue, efficiency, and risk reduction.
Advise leadership on data investment decisions, trade‑offs, and long‑term implications.
Contribute to development of the enterprise AI strategy and ensure its successful implementation across the organization.
Serve as the principal thought leader, for building, maintaining, and leading the AI Center of Excellence (COE).
Collaborate actively with Application Development, Infrastructure, and Cybersecurity Teams to ensure all models and guardrails meet corporate standards and compliance requirements.
Ensure alignment between data governance, model risk, and AI governance requirements, including controls over critical and high‑risk data elements.
Partner with Information Security to ensure data handling aligns with security, privacy, and regulatory requirements.
Leads the development, preparation, and execution of governance and performance materials.
Collaborates with relevant concerned parties to ensure data integrity of operations data within applicable databases for assigned account.
Define and monitor data quality metrics, thresholds, and remediation processes for critical data domains.
Ensure data issues that may impact risk, regulatory reporting, or decision‑making are identified, tracked, and escalated appropriately.
Support internal and external audits by providing evidence of data governance controls and reporting integrity.
Motivate and coach team members to assist them in exceeding their objectives.
Develop staffing plans anticipating short-, medium- and long-term needs.
Lead and participate in annual performance reviews, addressing areas of strength and improvement.