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About the Role

The Disney Decision Science and Integration (DDSI) team leverages advanced technologies, data analytics, and scientific approaches to build innovative tools that shape business decisions across The Walt Disney Company. This role will make key contributions to the Data Unification initiative by enabling rapid discovery and utilization of our vast data assets across the Disney enterprise.

Responsibilities

• Lead the design, development, and governance of enterprise ontologies, semantic layers, and knowledge graphs.
• Design and build enterprise ontologies and semantic models that align business objectives with technical implementation.
• Lead the creation and maintenance of semantic layer graphs, knowledge graphs, and entity graphs.
• Develop and implement strategies for using vector databases and graph databases to enable powerful LLM-augmented search and reasoning.
• Partner with data engineering, AI, and business teams to translate business goals into ontological models.
• Design and evolve the enterprise Data Catalog with rich semantic metadata, lineage, and governance capabilities.
• Contribute to the development of a unified data access layer that supports querying across multiple data sources through a single semantic interface.
• Implement and enforce enterprise security models through the ontology and semantic layer.
• Collaborate on AI integration initiatives, including building ontology-driven agents, RAG pipelines, and agent-to-agent communication protocols.
• Establish ontology governance processes, versioning, and lifecycle management.
• Contribute to the internal Data Marketplace by defining semantic data products.
• Mentor team members on semantic technologies and best practices.

Requirements

• 7+ years of experience in data engineering, data architecture, semantic technologies, knowledge engineering, or related fields with a strong technical implementation background.
• Deep expertise in ontology modeling (OWL, RDF, SKOS, SHACL) and graph technologies.
• Strong understanding of the differences between semantic layer graphs, knowledge graphs, and entity graphs and when to apply each.
• Hands-on experience with graph databases (Neo4j, Neptune, etc.) and vector databases for semantic search and LLM integration.
• Proficiency in designing and implementing Data Catalogs and semantic metadata management solutions.
• Experience building solutions on top of federated data architectures involving relational, document, object, and streaming systems.
• Demonstrated ability to translate complex business goals into ontological designs that accelerate delivery of business value.
• Strong programming skills, particularly Python, SPARQL, Cypher, GraphQL, and SQL.
• Experience with modern data platforms, cloud services (AWS preferred), and infrastructure-as-code practices.
• Solid understanding of data governance, security (RBAC/ABAC, dynamic masking), and compliance in enterprise environments.
• Excellent communication skills with the ability to bridge business stakeholders and technical teams.
• Experience building ontologies in large, complex enterprises (especially with media, entertainment, hospitality, or consumer-focused businesses).
• Hands-on experience with Generative AI, LLMs, RAG architectures, and agentic systems.
• Familiarity with data marketplace or data product platforms.
• Prior work with Apache Iceberg, Trino/Presto, or similar federated query engines.
• Knowledge of semantic web standards and tools (Protégé, TopBraid, Stardog, etc.).
• Background in formal knowledge representation, taxonomy development, or master data management.

Skills

• Ontology modeling (OWL, RDF, SKOS, SHACL)
• Graph technologies
• Graph databases (Neo4j, Amazon Neptune, Qdrant)
• Vector databases
• Data Catalogs
• Semantic metadata management
• Federated data architectures
• Python
• SPARQL
• Cypher
• GraphQL
• SQL
• AWS
• Infrastructure-as-code
• RBAC/ABAC
• Dynamic masking
• Generative AI
• LLMs
• RAG architectures
• Agentic systems
• Data marketplace platforms
• Apache Iceberg
• Trino/Presto
• Semantic web standards
• Protégé
• TopBraid
• Stardog
• Knowledge representation
• Taxonomy development
• Master data management

Location

• Lake Buena Vista, FL, USA
• Burbank, CA, USA
• New York, NY, USA
• Seattle, WA, USA

Work Type

• In office
• Full time

Experience Level

• 7+ years of experience

Education Level

• Bachelor’s degree and/or equivalent work experience
• Bachelor’s degree in Computer Science, Information Systems, Data Science, Philosophy (with logic focus), Linguistics, or a related technical field (or equivalent experience)
• Master’s degree or PhD in a relevant field (Semantic Technologies, AI, Data Science, or Computer Science)

Salary/Compensations

• Lake Buena Vista, FL: $148,300 to $198,800 per year
• Burbank, CA: $155,700 to $208,700 per year
• New York, NY or Seattle, WA: $163,100 to $218,700 per year

Benefits

• Bonus and/or long-term incentive units may be provided
• Full range of medical, financial, and/or other benefits

About the Company

• The Disney Decision Science and Integration (DDSI) team leverages advanced technologies, data analytics, and scientific approaches such as optimization and statistical modeling to build innovative tools that shape business decisions across The Walt Disney Company.
• We support Disney Entertainment (ABC, The Walt Disney Studios, Disney+, Hulu), ESPN, Disney Experiences (Theme Parks, Cruise Line, Consumer Products, DVC), and Corporate Finance with strategic applications that enable data-driven decision-making.