Data Ontology Engineer

General Dynamics Mission Systems, Inc•,

2d•$142,696 - $158,303•Remote

About The Position

General Dynamics Mission Systems (GDMS) is seeking a Data Ontology Engineer to design and maintain semantic schemas that describe key engineering and manufacturing entities and their relationships across systems. This role involves implementing ontologies using semantic web or graph technologies, building, querying, validating, and tuning knowledge graphs in production. You will integrate heterogeneous data sources into a common vocabulary, aligning schemas, code sets, and master data so AI services see one coherent picture. A key responsibility is designing the enterprise semantic layer that BI tools, analytics platforms, and AI/LLM applications query consistently, defining core business entities, metrics, and hierarchies and mapping them to existing data stores. You will also manage ontology governance, including versioning, documentation, reuse of industry standards, and enforcement of modeling best practices. This role directly feeds AI systems that make business decisions and requires collaboration with business SMEs and AI engineers, translating between these worlds. You will work across multiple enterprise domains, building a shared knowledge architecture.

Requirements

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
U.S. citizenship required.
Department of Defense Secret security clearance is required at time of hire.

Nice To Haves

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

Responsibilities

Design and maintain semantic schemas that describe key engineering and manufacturing entities — products, BOMs, plants, equipment, processes, work orders — and their relationships across systems.
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.
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.
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.
Manage versioning, documentation, reuse of industry standards, and enforcement of modeling best practices across pods.
Collaborate with business SMEs who understand the domain and AI engineers who consume your models.
Translate between business SMEs and AI engineers.