Head, Data & AI Enablement

The Head of Data & AI Platform Enablement defines and delivers the enterprise platform strategy for preparing, transforming, contextualizing, and activating data to support real-time, analytical, operational, and GenAI use cases. This role owns the end-to-end data and AI enablement platforms, spanning ingestion, transformation, semantic modeling, metadata, lineage, and observability across batch and streaming. The role ensures data is not only reliable and consumable, but also rich in context and meaning, enabling AI systems, analytics, and decisioning platforms to operate with accuracy, trust, and scale. Foundational capabilities such as metadata, lineage, semantic constructs, and operational signals are embedded directly into platform workflows to support AI reasoning, governance, diagnostics, and analytics without introducing friction for data producers and consumers., Leads and drives a customer focused culture throughout their team to deepen client relationships and leverage broader Bank relationships, systems and knowledge.

• Define and execute the enterprise Data & AI platform enablement strategy, spanning data ingestion, transformation, metadata, semantics, lineage, and observability to support real-time, analytical, operational, and GenAI use cases.
• Own the end-to-end lifecycle of enterprise data platforms across batch and streaming, from onboarding and configuration through execution, monitoring, and support, ensuring reliability, consistency, and usability for data producers and consumers.
• Drive standardized and repeatable patterns for publishing data from batch, streaming, CDC, and API-based sources into analytical and operational environments, with clear runtime expectations and consistent operational behavior.
• Own technical product direction for transformation platforms supporting batch and streaming processing, enabling teams to build, test, deploy, and operate transformations using standardized, supportable frameworks.
• Establish platform capabilities for schema management, schema evolution, and backward-compatible change handling to reduce downstream breakages while enabling rapid iteration.
• Define product guardrails and patterns for technical data quality checks (e.g., completeness, schema validation, anomaly indicators) and for surfacing results through consistent, platform-generated signals.
• Enable platform observability and operational intelligence by standardizing telemetry, run status reporting, error handling, retries, and idempotency to reduce incidents and accelerate diagnosis and recovery.
• Ensure ingestion and transformation platforms consistently produce analytics- and AI-ready datasets aligned with enterprise standards for naming, partitioning, data contracts, and consumption patterns.
• Partner closely with engineering, architecture, security, risk, and governance teams to ensure platforms meet enterprise requirements for data protection, access controls, retention, and auditability.
• Enable GenAI and AI platforms by providing governed access to well-contextualized data, metadata, lineage, and trust signals that support explainability, impact analysis, and safe AI usage.
• Use embedded observability and operational signals to identify recurring platform pain points, prioritize roadmap investments, and measure improvements in reliability, usability, and adoption over time.
• Lead platform modernization initiatives toward cloud-native architectures, evolving batch and streaming patterns while maintaining continuity for critical enterprise workloads.
• Translate complex platform capabilities into clear outcomes, tradeoffs, and roadmap sequencing for executive stakeholders, driving alignment across data engineering, analytics, AI, governance, and line-of-business leaders.
• Build and lead a high-performing product organization, establishing operating models, prioritization frameworks, and accountability for outcomes across data and AI enablement platforms.
• Understand how the Bank’s risk appetite and risk culture should be considered in day-to-day activities and decisions.
• Creates an environment in which their team pursues effective and efficient operations of their respective areas in accordance with Scotiabank’s Values, its Code of Conduct and the Global Sales Principles, while ensuring the adequacy, adherence to and effectiveness of day-to-day business controls to meet obligations with respect to operational, compliance, AML/ATF/sanctions and conduct risk.
• Builds a high-performance environment and implements a people strategy that attracts, retains, develops and motivates their team by fostering an inclusive work environment and using a coaching mindset and behaviors; communicating vison/values/business strategy; and, managing succession and development planning for the team.

Bachelor’s / College degree in business, finance, or technology

• Minimum 8 years of extensive experience in enterprise data platforms, data engineering, AI enablement, or technical product leadership roles.
• Bachelor’s degree in computer science, Engineering, or a related technical field.
• Proven ownership of platforms spanning data ingestion, transformation, metadata, and operational enablement.
• Strong understanding of distributed data systems, cloud data ecosystems, and the role of metadata and semantics in AI systems.
• Experience partnering with engineering, architecture, security, governance, and AI stakeholders in regulated enterprise environments.
• Ability to translate complex platform capabilities into clear product strategy, roadmaps, and executive-aligned outcomes.

Scotiabank is a leading bank in the Americas. Guided by our purpose: “for every future”, we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.