Basic Qualifications
Bachelor's degree in Systems Engineering, or a related Science, Engineering or Mathematics field, plus a minimum of 8 years of relevant experience; or Master's degree, plus a minimum of 6 years of relevant experience. CLEARANCE REQUIREMENTS: : Department of Defense Secret security clearance is required at time of hire. Applicants selected will be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. Due to the nature of work performed within our facilities, U.S. citizenship is required.
Responsibilities for this Position
What You'll Own
- Enterprise knowledge architecture. Define and maintain the overarching structure that connects domain-specific ontologies across pods. Manage the relationships between business vocabularies, taxonomies, and data models at the enterprise level.
- Cross-domain consistency. Ensure that business concepts defined in one pod are compatible with concepts in other pods. Resolve naming conflicts, semantic overlaps, and definitional inconsistencies before they become integration problems.
- Metadata and tagging standards. Establish enterprise-wide standards for metadata, tagging, classification, and search structures. Build the knowledge infrastructure that makes enterprise data findable, reusable, and machine-readable.
- Business glossary governance. Own the enterprise business glossary — the authoritative source for what terms mean across the organization. Work with data owners and business stewards to maintain accuracy.
- Knowledge repository architecture. Design the structures that store and expose enterprise knowledge — knowledge graphs, semantic catalogs, taxonomy services. Ensure AI agents can discover and traverse enterprise knowledge programmatically.
What You Won't Own
- Pod-specific data modeling — that's the Data/Ontology Engineer's role within each pod
- AI application development or engineering
- Enterprise system administration or data engineering pipelines
What Makes This Role Different
- You are building the connective tissue between multiple AI modernization efforts. Without your work, each pod builds an island. With it, they build a continent.
- Your knowledge architecture directly enables cross-domain AI reasoning. An agent that can connect HR data to manufacturing data to supply chain data — that capability starts with your architecture.
- This role requires both systems thinking and business fluency. You need to understand how manufacturing processes, HR workflows, and CRM systems relate at a business level, not just a data level.
Required Qualifications
- Bachelor’s degree in Systems Engineering, Computer Science, Information Science, or a related field, plus 8 years