The AI Data & Ontology Enablement Lead supports the implementation and adoption of data and ontology enablement practices and standards within Control Tower Operations to support scalable, governed, and business-aligned AI initiatives. Serving as the primary bridge between Business Units, Global IT, and Control Tower Operations, this role ensures shared understanding of data practices, workflows, and requirements by helping to translate established Global IT standards and operating models into repeatable guidance for Business Units and data stewards, while representing business domain needs back to IT. Through data steward education, workflow establishment, and close alignment with Control Tower governance processes, this role prevents fragmented, one-off data requests and ensures AI initiatives are supported by trusted data, shared meaning, and sustainable enterprise operations.

3.  KEY ACCOUNTABILITIES

Key Accountabilities

% of time

·     Enterprise Ontology Strategy & Enablement

o   Support the implementation and ongoing maintenance of ontology enablement practices and operating model strategy to support AI, analytics, and digital initiatives across multiple Business Units

o   Apply established standards for semantic modelling, domain alignment, concept reuse, and ontology lifecycle management.

o   Serve as the enterprise subject-matter authority for ontology-related topics, providing recommendations and guidance to governance and leadership forums.

o   Collaborate with Global IT and enterprise data architecture, to help ensure ontology practices align with enterprise data platforms and Control Tower operational processes.

30%

§  Business Unit Data & Ontology Enablement

o   Partner with Business Units to understand domain concepts, terminology, operational data, and AI use cases, translating them into ontology-aligned data structures.

o   Guide Business Units in contributing domain models, metadata, and data assets into the enterprise ontology using defined governance and intake processes.

o   Enable repeatable onboarding of Business Unit data into AI initiatives, reducing reliance on ad-hoc IT engagement and minimizing duplicated effort.

o   Provide input on prioritization of ontology enhancements based on organizational goals, AI roadmap needs, and enterprise value.

20%

§  Global IT Bridge & Translation

o   Serve as a liaison between Business Units and Global IT for AI data and ontology-related matters.

o   Engage with Global IT teams to understand enterprise data platforms, workflows, standards, and operational constraints.

o   Translate Global IT practices, requirements, and workflows into clear, actionable guidance for Business Unit data stewards.

o   Represent Business Unit data and ontology needs back to Global IT to inform platform evolution, tooling, and process improvements.

o   Prevent fragmented, one-by-one data requests by establishing shared understanding and standardized engagement models.

20%

§  Data Enablement & Workflow Establishment

o   Educate, guide, and support Business Unit data stewards on their roles in data governance, ontology contribution, and AI data enablement.

o   Support the development and documentation of workflows, expectations, and operating models for how BU data stewards engage with the Control Tower and Global IT.

o   Ensure Business Unit Data Stewards understand how to prepare, govern, and submit data assets for ontology integration and AI use.

o   Promote consistent adoption of governance, quality, and semantic standards across Business Units.

15%

§  Control Tower Operations Alignment

o   Support integration of data and ontology enablement into Control Tower workflows.

o   Provide operational insight into data readiness, semantic risks, and governance gaps to inform Control Tower decision-making.

o   Identify systemic issues and contribute recommendations to drive continuous improvement of data enablement processes.

o   Assist teams in improving understanding and utilization of data in AI processes.

10%

§  Semantic Integrity, Data Quality & Risk Management

o   Ensure semantic integrity, data quality, lineage, and consistency are maintained as data assets flow into AI solutions.

o   Identify systemic issues and recommend continuous improvement opportunities to Control Tower Operations leadership.

o   Influence corrective actions, tooling investments, or governance updates to mitigate long-term risk.

5%

4. QUALIFICATIONS, EXPERIENCE, & SKILLS

Minimum Educational Qualifications

§  Bachelor’s degree in Computer Science, Data Science, Engineering, or related field.

Minimum Experience

§  10+ years based on education of combined experience in data architecture, data governance, ontology development, semantic modelling, or related disciplines, supporting cross-functional initiatives spanning multiple business units and IT organizations.

Other Job-Specific Skills

§  Deep expertise in ontology design, semantic modelling, and domain-driven data architecture

§  Experience contributing to the development and implementation of data and ontology strategies.

§  Demonstrated experience serving as a bridge between business stakeholders and IT organizations

§  Strong ability to translate technical platforms, workflows, and constraints into business-understandable guidance

§  Experience enabling and educating data stewards and establishing sustainable governance workflows

§  Ability to influence senior technical and business stakeholders without direct authority

§  Sound judgment in balancing short-term delivery needs with long-term semantic and architectural sustainability

§  Systems-thinking mindset with understanding of AI, analytics, and enterprise data platforms

§  Comfort operating in ambiguous, evolving environments with enterprise-level impact

5. KEY PERFORMANCE INDICATORS

§ Adoption of enterprise ontology standards across AI and data initiatives

§ Reduction in fragmented or ad-hoc IT data requests from Business Units

§ Business Unit and data steward participation in ontology workflows

§ Reuse of ontology-aligned data assets across multiple AI use cases

§ Stakeholder feedback on clarity, effectiveness, and scalability of data enablement processes

§ Reduction in rework caused by inconsistent data definitions or semantic misalignment

6. COMMUNICATIONS & WORKING RELATIONSHIPS

Internal Contacts

§  Business Units & Data Stewards

o   Enable domain understanding, data readiness, ontology contribution, and AI data governance.

§  AI Strategist

o   Align data and ontology enablement priorities with enterprise AI strategy and standards.

o   Provide input on data readiness, semantic constraints, and systemic risks that may impact strategic planning or sequencing.

§  Foundry Lead

o   Coordinate with delivery leadership to ensure data and ontology enablement standards are understood and incorporated into solution development, without direct involvement in delivery execution.

§  Control Tower Operations Lead

o   Align ontology enablement in AI projects to ensure enterprise AI strategy, standards, and priorities are operationalized within established governance, risk, compliance, and value-realization processes

§  Global IT (Data Platforms, Architecture, Operations)

o   Understand enterprise systems, workflows, and standards; communicate business needs and influence platform evolution.

§  Risk & Compliance Specialist

o   Ensure ontology and data practices align with enterprise risk and compliance requirements.

External Contacts

§  Technology Vendors / Standards Bodies (as needed)

o   Engage selectively with external partners, vendors, or industry forums to stay informed on emerging AI trends, regulatory considerations, and best practices that may inform enterprise AI strategy.

8.  DECISIONS EXPECTED

§ Provide recommendations to support the evolution of enterprise ontology enablement standards, structures, and governance workflows.

§ Evaluate Business Unit domain models and data assets and recommend how they may be integrated into the enterprise ontology framework.

§ Identify ontology, semantic, or data governance risks and recommend escalation or architectural intervention.

§ Provide input and recommendations to inform prioritization of data and ontology investments based on enterprise impact.