Data Engineer - Ontology Engineering

Key Responsibilities:

Ontology Development:

• Design and implement ontologies based on BFO and CCO principles, ensuring alignment with business needs and industry standards.
• Collaborate with domain experts to capture and formalize domain knowledge into ontological structures.
• Develop and maintain comprehensive ontologies modeling business entities, relationships, and processes.

Data Modeling:

• Design and implement semantic and syntactic data models adhering to ontological principles.
• Create scalable, flexible, and adaptable data models to meet evolving business requirements.
• Integrate data models with existing data infrastructure and applications.

Knowledge Graph Implementation:

• Design and build knowledge graphs grounded in ontologies and data models.
• Develop algorithms and tools for knowledge graph population, enrichment, and maintenance.
• Utilize knowledge graphs to support advanced analytics, search, and recommendation systems.

Data Quality and Governance:

• Ensure quality, accuracy, and consistency across ontologies, data models, and knowledge graphs.
• Define and enforce data governance processes and standards for ontology development and upkeep.

Collaboration and Communication:

• Work closely with data scientists, software engineers, and business stakeholders to understand data requirements and deliver tailored solutions.
• Clearly communicate complex technical concepts to varied audiences.

Qualifications:

Education:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related discipline.

Experience:

• 5+ years in data engineering or a related field.
• Proven experience in ontology development using BFO, CCO, or similar frameworks.
• Strong knowledge of semantic web technologies: RDF, OWL, SPARQL, SHACL.
• Proficiency in Python, SQL, and other relevant programming languages.
• Experience with graph databases (e.g., TigerGraph, JanusGraph) and triple stores (e.g., GraphDB, Stardog) is a plus.

Desired Skills:

• Familiarity with machine learning and natural language processing.
• Experience with cloud data platforms such as AWS, Azure, or GCP.
• Exposure to Databricks technologies like Spark, Delta Lake, Iceberg, Unity Catalog, UniForm, and Photon.
• Strong analytical and problem-solving skills.
• Excellent communication and interpersonal abilities.