Manager, Data & AI Governance & Enablement

Position Overview: 

The Manager, Data & AI Governance & Enablement is accountable for designing, implementing, and continuously improving WestJet's enterprise data and AI governance capabilities. The role turns governance requirements into practical processes, tools, controls, and guidance that support the responsible use of data and AI. 

The Manager leads the Data & AI Governance & Enablement team and is responsible for activating governance, ensuring requirements are embedded into business processes, delivery practices, platforms, and decision-making rather than existing only as policies or documentation. The role partners closely with business leaders, data owners, technology teams, Legal, Privacy, Cybersecurity, and delivery teams to establish clear accountabilities, improve trust in data, govern AI use, and enable responsible innovation.

The position supports the enterprise objective of helping WestJet use data and AI safely, effectively, and with confidence while maintaining compliance with applicable legal, regulatory, and internal requirements.

Key Responsibilities: 

Evolve and improve the enterprise data governance framework, roadmap, standards, and operating model

Drive adoption of data ownership and stewardship accountabilities across business domain

Integrate data governance requirements into existing ways to work, enhancing processes and controls to ensure governance practices are practical, scalable and consistently applied

Coordinate governance forums, issue escalation processes, exception management, metrics, and reporting

Drive continuous improvement of governance capabilities through measurable outcomes and maturity advancement

Define enterprise data quality dimensions, standards, measurement frameworks, monitoring practices, and improvement expectations.

Establish governance practices for metadata, lineage, and business glossary management.

Partner with Cybersecurity and business stakeholders to operationalize data classification, handling, and protection requirements, embedding them into technology platforms, delivery processes, and day-to-day operations. 

Ensure data retention, archival, and disposal requirements are translated into practical processes, controls, and system capabilities that are consistently applied across the organization.

Drive adoption of lifecycle management practices that improve trust, consistency, accountability and regulatory compliance across enterprise data assets.

Operationalize data use guardrails and decision rights across the enterprise

Embed governance requirements for data access, sharing, and consumption into business and technology processes.

Provide governance oversight of enterprise data use and sharing practices, supporting data owners and stewards in managing domain-specific risks and obligations

Partner with Privacy, Cybersecurity, business, and technology teams to drive consistent adoption of governance practices.

Define and operationalize the enterprise AI governance framework.

Integrate AI governance into existing intake, procurement, delivery, and risks processes, ensuring AI-enabled initiatives are identified and routed through appropriate AI reviews and controls

Govern AI use cases, model risk, fairness, explainability, transparency, human oversight, and responsible AI requirements.

Coordinate AI assessments, risk classification, multidisciplinary review, approvals, exception management, and issue escalation for AI-enabled initiatives and solutions

Maintain oversight of enterprise AI systems, governance evidence, documentation requirements, and lifecycle monitoring

Provide governance oversight for internally developed and third-party AI solutions, automated decision-making capabilities, and emerging AI technologies. 

Monitor emerging data governance, AI governance, and regulatory requirements in partnership with Privacy and Legal teams

Translate regulatory obligations into practical controls and business practices embedded within existing processes, where possible.

Assess governance implications of new technologies, vendor solutions, data initiatives, and AI investments.

Support audit readiness, governance reporting, control monitoring, and continuous improvement of governance practices.

Develop and coordinate the execution of the enterprise AI literacy strategy in partnership with HR, Communications, and Technology teams.  

Equip data owners, stewards, product teams, and business stakeholders with practical guidance, templates, tools, and self-service resources.

Drive adoption of governance practices by connecting requirements to business outcomes, delivery processes, and employee experience.

Promote a culture of responsible data and AI use across the organization.

Lead the implementation and continuous improvement of governance tooling and platforms.

Establish reporting and performance measures that provide visibility into governance obligations, decisions, risks, controls, adoption, and AI value realization.

Develop dashboards, scorecards, and executive reporting to support governance oversight and track value delivered through data and AI investments.

Build, lead, coach, and develop a high-performing team responsible for data governance, AI governance, and enablement activities. 

Establish clear priorities, goals, performance expectations, and development plans for team members. 

Foster a culture of accountability, collaboration, continuous improvement, and responsible innovation.

Provide mentorship and professional development opportunities that strengthen governance, risk, data, and AI capabilities across the team.

Manage team capacity, work prioritization, vendor relationships, and delivery commitments.

Qualifications

Bachelor’s degree in business, technology, Information Management, Data Management, Computer Science, or a related field.

Advanced degrees are considered an asset.

Relevant professional certifications in data governance, information management, AI governance, risk management, compliance, or related disciplines are preferred.

8+ years of progressive experience across data governance, AI governance, regulatory compliance, data management, information management, risk management, or related leadership roles.

Experience designing and implementing enterprise governance frameworks, policies, standards, controls, and operating models

Experience operationalizing data ownership, stewardship, data quality, classification, metadata, lineage, and lifecycle management capabilities.

Experience implementing AI governance practices, including use-case intake, risk assessment, approvals, inventory management, governance documentation, and control monitoring

Experience partnering with Legal, Privacy, Cybersecurity, Technology, and business teams to operationalize governance requirements. 

Experience leading organizational change, enablement programs, literacy initiatives, and adoption efforts.

Experience leading and developing high-performing teams.