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.
Responsibilities for this PositionWhat You'll Own
- Domain ontologies. Design and maintain semantic schemas that describe key engineering and manufacturing entities — products, BOMs, plants, equipment, processes, work orders — and their relationships across systems.
- Knowledge graphs. Implement ontologies using semantic web or graph technologies (RDF/OWL/SHACL/SPARQL or property-graph equivalents like Neo4j). Build, query, validate, and tune knowledge graphs in production.
- Data alignment. Integrate heterogeneous data sources — PLM, ERP, MES, CMMS, QMS, data lakes — into a common vocabulary. Align schemas, code sets, and master data to the ontology so AI services see one coherent picture.
- Semantic layer. Design the enterprise semantic layer that BI tools, analytics platforms, and AI/LLM applications query consistently. Define core business entities, metrics, and hierarchies and map them to existing data stores.
- Ontology governance. Manage versioning, documentation, reuse of industry standards, and enforcement of modeling best practices across pods. Your ontologies are shared assets — they must be maintainable by others.
What You Won't Own
- AI model development or prompt engineering — you provide the data substrate, the AI engineers build on it
- Enterprise system administration — you integrate data from systems, you don't manage them
- Business process decisions — Domain SMEs and the Product Owner define what matters; you model it
What Makes This Role Different
- Your ontologies directly feed AI systems that make real business decisions. A bad data model doesn't just slow a report — it makes an AI agent reason incorrectly.
- You will work across multiple enterprise domains — HR, manufacturing, CRM, supply chain — building a shared knowledge architecture, not siloed data models.
- You will collaborate with business SMEs who understand the domain and AI engineers who consume your models. You translate between both worlds.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Science, Information Science, or a related field, plus 5 years of experience; or Master’s degree plus 3 years of experience
- Hands-on experience with knowledge graph or ontology technologies — RDF/OWL/SHACL/SKOS, SPARQL, and/or graph databases (Neo4j, Stardog, Ontotext, AWS Neptune, or similar)
- Experience integrating disparate enterprise data sources into a shared vocabulary or knowledge graph — you have aligned data across systems that use different schemas, code sets, and terminology
- Strong data modeling skills — dimensional modeling, semantic modeling, or formal ontology design applied in production, not just academic settings
- Experience with enterprise data platforms — data warehouses, data lakes, Snowflake, Palantir Foundry, or similar
- S. citizenship required. Department of Defense Secret security clearance is required at time of hire.
Preferred Qualifications
- Experience building semantic layers or metrics layers consumed by BI, analytics, or AI/LLM applications
- Experience with enterprise systems data (ERP, MES, PLM, CRM) — you understand the data structures these systems produce
- Familiarity with AI/ML data requirements — embeddings, vectorization, retrieval-augmented generation, and how knowledge graphs support LLM reasoning
- Comfortable leading workshops with non-technical business SMEs to capture requirements and iteratively refine data models
- Experience with ontology governance — versioning, documentation, standards reuse across teams or an enterprise
What Sets You Apart
- You think in relationships, not rows. You see connections between data that others model as flat tables.
- You can explain a semantic model to a business SME and have them recognize their domain in it.
- You build ontologies that other people can use and extend — not elegant models that only you understand.
- You have integrated data from systems that were never designed to work together and made it coherent.
- You care about data meaning, not just data structure. You know that two systems calling something "part number" doesn't mean they mean the same thing.
Details
- Remote — 100% telework
- 9/80 schedule
- Defense industry experience is not required
Salary NoteThis estimate represents the typical salary range for this position based on experience and other factors (geographic location, etc.). Actual pay may vary. This job posting will remain open until the position is filled.
Combined Salary RangeUSD $142,696.00 - USD $158,303.00 /Yr.
