Position Summary 

Marriott International is seeking a strategic and execution-focused leader to serve as Senior Director, AI Data within the Data Services organization. Reporting to the Managing Vice President, Data Services, this role will lead the design and delivery of the AI-ready data foundation on which enterprise AI products, analytics, personalization, and intelligent experiences depend. 

This leader will be responsible for transforming Marriott data into trusted, contextualized, reusable, and safely consumable assets for both human users and AI systems. The role brings together enterprise semantic models, knowledge graphs, metadata, data products, feature and context services, and governed access patterns that allow AI systems to discover, interpret, and use enterprise information with confidence. 

This is a build-and-transform mandate within Data Services, requiring strong partnership across Data Platform, Data Engineering, Data Governance, AI/ML, Digital, Loyalty, Operations, Commercial, and corporate functions. The Senior Director will help define a multi-year roadmap for AI Data and deliver the capabilities sequentially with measurable business impact. 

Expected Contributions 

Enterprise Ontology & Semantic Layer 

• Define and govern the shared vocabulary of the enterprise so systems, models, analytics, and AI agents operate from consistent definitions of core concepts such as guest, property, stay, transaction, loyalty interaction, reservation, and operational event. 

• Lead the development of business-aligned semantic models and common data definitions that improve interoperability, reuse, and decision consistency across Marriott. 

• Partner with domain leaders and governance teams to ensure semantic assets are owned, maintained, and embedded into data products and consumption experiences. 

Connected Knowledge Graph 

• Move beyond rows-and-tables thinking toward a relationship-first intelligence layer that connects guest signals, property attributes, loyalty behavior, digital interactions, commercial activity, and operational events. 

• Lead the design and delivery of knowledge graph capabilities that allow AI systems and analysts to reason across relationships, context, and enterprise entities. 

• Prioritize high-value graph use cases that support personalization, service recovery, operational intelligence, marketing effectiveness, and decision automation. 

Agent-Discoverable Metadata 

• Create metadata capabilities that make data assets machine-readable, discoverable, trusted, and usable by AI systems with appropriate human oversight. 

• Expand metadata coverage across business glossary, lineage, freshness, ownership, quality, usage, and access classifications. 

• Partner with Data Governance and Data Platform teams to embed metadata as a core service for AI, analytics, and enterprise data consumption. 

MCP Servers & Agent APIs 

• Shape governed interfaces through which internal and partnered AI agents can query enterprise knowledge, retrieve context, and trigger approved actions. 

• Partner with AI, platform, security, privacy, and architecture teams to establish Model Context Protocol patterns, agent APIs, auditability, and policy controls. 

• Ensure AI consumption patterns are designed for scale, transparency, access control, and responsible enterprise use. 

Real-Time Knowledge Movement 

• Advance event-driven and freshness-aware data capabilities so knowledge assets and downstream AI consumers reflect current reality rather than stale snapshots. 

• Partner with engineering and platform teams to reduce batch dependencies where near-real-time context is required for AI and business decisioning. 

• Drive standards for freshness, observability, reliability, and operational readiness across priority AI Data assets. 

AI Data Products & Context Services 

• Establish reusable AI-ready data products, features, embeddings, vectorized assets, and context services that accelerate delivery of GenAI, ML, personalization, analytics, and agentic solutions. 

• Promote domain-oriented ownership and lifecycle management for data products used by AI and advanced analytics teams. 

• Reduce duplication and improve speed-to-market by standardizing how context and reusable intelligence assets are created, managed, and consumed. 

Candidate Profile 

Education and Experience Required 

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Management, Business, or a related field. 

• Ten or more years of experience in knowledge engineering, enterprise data, analytics, applied-AI platforms, data architecture, or related disciplines. 

• At least five years of experience owning end-to-end delivery of enterprise-scale data, AI, analytics, or platform capabilities. 

• Demonstrable experience designing, delivering, or operating one or more of the following: enterprise ontologies, semantic layers, production knowledge graphs, metadata platforms, feature stores, or real-time data infrastructure. 

• Experience leading cross-functional teams and complex transformation efforts in a large, matrixed, global organization. 

• Working fluency with the agentic-AI stack, including model context interfaces, retrieval architectures, vector stores, graph stores, metadata-driven systems, and enterprise governance patterns. 

• Experience with AI-ready data capabilities such as context layers, semantic models, vectorized data assets, feature stores, knowledge graphs, and governed APIs. 

Preferred Qualifications 

• Strong understanding of data privacy, security, access controls, lineage, data quality, stewardship, and responsible AI practices. 

• Proven ability to influence executive stakeholders and communicate with clarity on data strategy, AI readiness, risk, architecture, and business value. 

• Comfort operating with senior stakeholders including executives, audit, risk, legal, privacy, owners, franchise partners, and business leaders. 

• Track record of hiring, developing, and retaining senior technical, product, and data talent. 

• Governed agent access patterns support responsible, auditable, and scalable AI consumption across internal and partnered use cases.