Lead Data and Ontology Engineer

Job Posting Title: Lead Data and Ontology Engineer Req ID: 10153229 Job Description: 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. This team works in office. What You Will Do As the Ontology/Semantic Librarian, you will lead the design, development, and governance of enterprise ontologies, semantic layers, and knowledge graphs that serve as the intelligent backbone for a modern federated data platform. This role combines deep semantic modeling expertise with hands-on implementation of graph technologies, vector databases, and federated data architectures to accelerate business value delivery. 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. Design and build enterprise ontologies and semantic models that align business objectives with technical implementation, ensuring rapid enablement of business use cases such as unified reporting, AI agents, data marketplaces, and cross-functional insights. Lead the creation and maintenance of semantic layer graphs, knowledge graphs, and entity graphs, clearly distinguishing and leveraging the strengths of each approach. Develop and implement strategies for using vector databases and graph databases (e.g., Neo4j, Amazon Neptune, Qdrant, or similar) to enable powerful LLM-augmented search and reasoning over highly federated, heterogeneous data stores. Partner with data engineering, AI, and business teams to translate business goals into ontological models that drive measurable outcomes. 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 Snowflake, Databricks, PostgreSQL, MongoDB, S3, Kafka, and other sources through a single semantic interface. Implement and enforce enterprise security models (RBAC/ABAC, column/row-level security, and dynamic masking) 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 to ensure scalability and consistency across the enterprise. Contribute to the internal Data Marketplace by defining semantic data products with clear business value and consumption models. Mentor team members on semantic technologies and best practices for ontology-driven data architecture. Required Qualifications & Skills 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 (PostgreSQL, Snowflake), document (MongoDB), object (S3), and streaming (Kafka) 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. Desired Qualifications 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. Required Education Bachelor’s degree in Computer Science, Information Systems, Data Science, Philosophy (with logic focus), Linguistics, or a related technical field (or equivalent experience). Preferred Education Master’s degree or PhD in a relevant field (Semantic Technologies, AI, Data Science, or Computer Science). #DISNEYTECH #DisneyAnalytics The hiring range for this position in Lake Buena Vista, FL is $148,300 to $198,800 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered. Job Posting Segment: Corporate Strategy Job Posting Primary Business: Decision Science & Integration Primary Job Posting Category: Data Engineering Employment Type: Full time Primary City, State, Region, Postal Code: Lake Buena Vista, FL, USA Alternate City, State, Region, Postal Code: Date Posted: 2026-06-25 Learn more about us.