Job Description Summary: The Senior Manager - Ontology and Graph Modeling play a pivotal role in building the semantic foundations that drive consistent, trusted, and actionable data across our global system. This role will be part of a forward-looking Data Engineering and Platforms team, enabling scalable use of trusted data, advanced analytics, and knowledge graphs to power decision-making.
Semantic clarity is essential for interoperability across markets, AI models, and platforms. This role will drive the creation of a governed semantic and graph foundation that connects fragmented data sources and enables agents, copilots, analytics, and operational decision-making. This is an individual contributor role focused on hands-on technical leadership, solution design, and delivery excellence rather than direct people management.
Core Responsibilities - Lead the design, development, and maintenance of enterprise ontologies, taxonomies, controlled vocabularies, and graph models to enable semantic consistency and interoperability.
- Define modeling standards, reusable patterns, and implementation strategies for ontologies, entity relationships, upper ontology concepts, and property graph structures.
- Integrate graph solutions with enterprise data stores, APIs, MCP servers, and related technologies to meet stakeholder needs.
- Architect scalable mapping pipelines that connect distributed physical data sources to the logical graph layer without data redundancy.
- Enable AI and machine learning through structured knowledge representations that improve inference, entity resolution, and data discoverability.
- Use LLMs, GenAI, rules engines, reusable frameworks, and automation utilities to curate, build, adapt, and evolve the corporate ontology catalog.
- Implement semantic validation, formal reasoning, and performance monitoring frameworks to ensure model correctness, scalability, auditability and reliability.
- Design semantic layers that explicitly bind underlying physical data tables to the enterprise ontology, ensuring autonomous agents and subagents are grounded in deterministic business logic rather than probabilistic LLM outputs.
- Develop context-injection and semantic routing patterns that allow multi-agent systems to securely query and traverse the knowledge graph for complex, multistep reasoning and planning.
- Establish the graph model as the foundational long-term memory and context engine for enterprise copilots, enabling agents to maintain state and context across disjointed user sessions.
Required Qualifications & Experience - Bachelor's or master's degree in information science, library science, ontology, semantics, computational linguistics, computer science, or related field.
- 2+ years of experience defining and implementing production-grade knowledge graphs and ontologies.
- Ability to develop and implement ontologies and data models in collaboration with stakeholders across data management, search, product management, machine learning, and other enterprise initiatives.
- 3+ years of experience with knowledge graph technologies such as RDF, OWL, SHACL, SKOS, LPG, and SPARQL.
- At least 2 years of experience or training with ontology and linked data tools such as ProtE9gE9, TopQuadrant, Stardog, Jena, or Data.World.
- Expert proficiency with graph query languages such as Cypher, GQL, or SPARQL.
- Familiarity with enterprise ontology management suites and governance frameworks, including Knowledge Graph (organizational, GraphRAG, Query/Traversal) patterns.
- Hands-on experience integrating knowledge graphs with LLM orchestration and agent frameworks (e.g., LangChain, AutoGen, Semantic Kernel) to build productiongrade GraphRAG pipelines.
- Proficiency in hybrid retrieval strategies, combining vector embeddings with graph traversals to optimize agent context windows.
Preferred Qualifications - Understanding of the development of ontologies and the use of controlled vocabularies and thesauri in enhancing the discovery of management of enterprise data.
- Experience designing architectures that manage parallel, autonomous AI subagents, utilizing the graph to enforce boundaries and prevent conflicting actions.
- Familiarity with exposing graph traversal functions as distinct 'tools' or 'skills' for LLM tool-calling (e.g., via OpenAI function calling or MCP servers).
- Experience with Palantir, Microsoft Fabric and Microsoft Foundry.
Success Measures- Evaluate the current state of semantic pilots, data assets, and structural mappings across the organization.
- Standardize the core taxonomical conventions and architectural blueprints for initial multi-domain integration.
- Demonstrate a measurable reduction in AI hallucination rates and a quantifiable increase in autonomous multi-step task completion by leveraging the governed semantic foundation.
- Showcase a quantifiable increase in context-retrieval accuracy and performance for dependent enterprise AI applications.
Skills:Pay Range:United States of America: 152,000 USD - 178,300 USD
Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:15
Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):United States of America
City/Cities:Atlanta
Travel Required:00% - 25%
Relocation Provided:No
Job Posting End Date:August 14, 2026
