Ontology Data Modeler
MUST HAVES
• Bachelor's Degree in a technical field
• 5+ years of experience working in data modeling
• Skilled in designing conceptual, logical and physical data models to accurately represent data structures
• Proven track record in driving source systems, generating models, and supporting the business needs from a data perspective
• Proficient with data modeling tools such as erwin Data Modeler, ER/Studio, or IBM Data Architect
• 2+ years of hands-on experience with Ontology Design and Implementation
• Skilled in creating semantic data models, reasoning engines, validation rules, developing semantic metadate and tagging strategies, and maintaining knowledge graphs
• Familiarity with Ontology tools like Protégé, TopBraid Composer, and FluentEditor
• Proficient in Ontology languages such as OWL (Web Ontology Language), RDF (Resource Description Framework), and SHACL (Shapes Constraint Language)
• Strong experience in AWS Cloud, Python, and SQL
• Demonstrated ability to articulate ideas clearly, effectively engage with stakeholders, and communicate the process and benefits of ontology creation
PLUSSES
• Hands-on experience with AWS Glue
• 3+ years of experience with Spark, Kafka or other big data processing frameworks
• Experience with Informatica, Talend or Apache NiFi
• Prior experience with AWS GovCloud, Data Catalogue or AWS RedShift
• Experience in the aviation industry or other regulated industries
DAY TO DAY
• Design and implement ontologies that represent manufacturing processes, part configurations, supply chain hierarchies, logistics, quality events, and compliance frameworks.
• Collaborate with domain experts across engineering, quality, logistics, and IT to formalize vocabulary, terminology, and relationships using OWL, RDF, SHACL, and similar standards.
• Develop and maintain knowledge graphs that integrate data from MES, ERP, PLM, QMS, and IoT sources.
• Establish semantic data models to support digital twin, root cause analysis, predictive quality, and sustainability efforts.
• Support interoperability between systems and across tiers of suppliers by aligning with standards such as ISO 8000, QIF, ISO 10303 (STEP), and others.
• Implement and tune reasoning engines and validation rules to infer product dependencies, identify anomalies, and ensure data integrity.
• Drive the development of semantic metadata and tagging strategies to power advanced search, lineage tracing, and knowledge reuse.
• Align ontological structures with enterprise data strategies and digital engineering initiatives, including model-based systems engineering (MBSE).
• Contribute to knowledge governance and ontology lifecycle management processes.
MUST HAVES
• Bachelor's Degree in a technical field
• 5+ years of experience working in data modeling
• Skilled in designing conceptual, logical and physical data models to accurately represent data structures
• Proven track record in driving source systems, generating models, and supporting the business needs from a data perspective
• Proficient with data modeling tools such as erwin Data Modeler, ER/Studio, or IBM Data Architect
• 2+ years of hands-on experience with Ontology Design and Implementation
• Skilled in creating semantic data models, reasoning engines, validation rules, developing semantic metadate and tagging strategies, and maintaining knowledge graphs
• Familiarity with Ontology tools like Protégé, TopBraid Composer, and FluentEditor
• Proficient in Ontology languages such as OWL (Web Ontology Language), RDF (Resource Description Framework), and SHACL (Shapes Constraint Language)
• Strong experience in AWS Cloud, Python, and SQL
• Demonstrated ability to articulate ideas clearly, effectively engage with stakeholders, and communicate the process and benefits of ontology creation
PLUSSES
• Hands-on experience with AWS Glue
• 3+ years of experience with Spark, Kafka or other big data processing frameworks
• Experience with Informatica, Talend or Apache NiFi
• Prior experience with AWS GovCloud, Data Catalogue or AWS RedShift
• Experience in the aviation industry or other regulated industries
DAY TO DAY
• Design and implement ontologies that represent manufacturing processes, part configurations, supply chain hierarchies, logistics, quality events, and compliance frameworks.
• Collaborate with domain experts across engineering, quality, logistics, and IT to formalize vocabulary, terminology, and relationships using OWL, RDF, SHACL, and similar standards.
• Develop and maintain knowledge graphs that integrate data from MES, ERP, PLM, QMS, and IoT sources.
• Establish semantic data models to support digital twin, root cause analysis, predictive quality, and sustainability efforts.
• Support interoperability between systems and across tiers of suppliers by aligning with standards such as ISO 8000, QIF, ISO 10303 (STEP), and others.
• Implement and tune reasoning engines and validation rules to infer product dependencies, identify anomalies, and ensure data integrity.
• Drive the development of semantic metadata and tagging strategies to power advanced search, lineage tracing, and knowledge reuse.
• Align ontological structures with enterprise data strategies and digital engineering initiatives, including model-based systems engineering (MBSE).
• Contribute to knowledge governance and ontology lifecycle management processes.
