Data Ontology Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 13, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Data Ontology Engineer UNAVAILABLE, UNAVAILABLE, US 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 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 dat

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