Crown Point Technologies, LLC Knowledge Engineer Columbia, MD · Reston, VA · Washington, DC · Full time

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Crown Point Technologies, LLC is an Economically Disadvantaged Women-Owned Small Business (EDWOSB) dedicated to providing critical full-stack software development and data engineering solutions and services to both Government and commercial clients. Proudly based in Maryland, our small business is dedicated to serving customers in a variety of domains including DoD, Federal Health, Life Sciences, Financial Services, and Manufacturing.

Crown Point Technologies is seeking a Knowledge Engineer to support a Department of War (DoW) customer focused on digital engineering, semantic modeling, and knowledge graph technologies.

In this role, you will help design and maintain semantic models that connect complex engineering information across systems, data sources, and lifecycle activities. You will work closely with engineers, subject matter experts, and technical teams to translate engineering concepts and source-system data into formal ontologies and canonical knowledge models.

This is an ideal opportunity for someone who enjoys solving complex modeling problems, learning new technical domains, and using semantic technologies to make highly interconnected engineering data more discoverable, understandable, and useful.

Responsibilities

As a Knowledge Engineer, you will:

• Develop, extend, and maintain domain and application ontologies supporting engineering and the engineering lifecycle.
• Align ontology classes and properties with established upper-level ontologies, including Basic Formal Ontology (BFO) and Common Core Ontologies (CCO).
• Evaluate existing ontology terms before introducing new classes or properties and determine appropriate placement within established ontology hierarchies.
• Translate terminology and concepts from customer systems and source data into canonical semantic models.
• Develop crosswalks and mappings between source-system concepts and canonical ontology classes and properties.
• Analyze structured and semi-structured engineering data to determine appropriate semantic representations.
• Develop complex SPARQL queries supporting data transformation, mapping, integration, validation, and analysis.
• Integrate information from APIs and other enterprise data sources into knowledge graph environments.
• Troubleshoot semantic mappings, data transformations, SPARQL queries, and ingestion pipelines.
• Independently research and learn unfamiliar engineering, manufacturing, product lifecycle, and systems concepts necessary to support ontology development.
• Maintain ontology documentation, mapping specifications, modeling decisions, and other semantic-model documentation.
• Collaborate with engineers, data professionals, and domain subject matter experts to resolve terminology, data-modeling, and semantic-integration questions.
• Communicate semantic models and technical concepts clearly to both technical and non-technical stakeholders.

Required Qualifications

• Experience developing or maintaining ontologies, semantic models, knowledge graphs, or other formal data models.
• Strong understanding of RDF, RDFS, OWL, and SPARQL.
• Experience developing complex SPARQL queries for querying, transformation, mapping, integration, or validation.
• Understanding of ontology modeling concepts such as classes, properties, relationships, restrictions, logical axioms, and controlled vocabularies.
• Ability to analyze complex technical domains and translate domain knowledge into formal semantic representations.
• Strong analytical, critical-thinking, and conceptual-modeling skills.
• Ability to independently research and understand unfamiliar technical or engineering concepts.
• Experience working with heterogeneous data sources and developing mappings to canonical data or semantic models.
• Strong technical writing and documentation skills.
• Ability to communicate complex technical concepts to technical and non-technical audiences.
• Ability to collaborate effectively with engineers and domain subject matter experts to resolve terminology and modeling questions.

Preferred Qualifications

Experience in one or more of the following areas is preferred:

• An active U.S. Government security clearance is highly desirable, but not required.
• Basic Formal Ontology (BFO) and Common Core Ontologies (CCO).
• SHACL and RDF graph validation.
• SKOS and controlled vocabulary development.
• Enterprise knowledge graph platforms such as Altair Graph Studio, Anzo, or comparable technologies.
• Ontology development tools such as Mobi, Protégé, or similar platforms.
• Integrating REST APIs and external data sources into semantic or knowledge graph environments.
• Systems engineering and Model-Based Systems Engineering (MBSE).
• SysML, UAF, requirements management, digital engineering, or product lifecycle data.
• Interpreting engineering standards and translating them into formal semantic or data-model requirements.
• Experience supporting DoW, Intelligence Community, federal government, aerospace, manufacturing, or other complex engineering environments.

What We're Looking For

Successful candidates are naturally curious and comfortable working in technical areas where the answer may not already be defined. You should enjoy investigating unfamiliar concepts, identifying relationships between information, and developing logical models that accurately represent complex real-world systems.

You do not need to be a subject matter expert in every engineering discipline on day one. We are looking for someone with a strong semantic-modeling foundation, the ability to quickly learn new domains, and the willingness to work closely with subject matter experts to build accurate, useful knowledge models.

Candidates who currently hold an active U.S. Government security clearance are strongly encouraged to apply, although a clearance is not required for consideration.