Lead Knowledge Engineer - Semantic Web Technologies
S&P Global
Gdansk, Poland
Not specified; not specified; competitive pyy + co...
Hybrid
Ontology development experience
Semantic web technologies rdf owl sparql
Knowledge graph construction and maintenance
The role involves developing and enhancing ontologies, taxonomies, and knowledge graphs to support interconnected data strategies within S&P Global's Enterprise Data Organization
Job Summary
The role involves developing and enhancing ontologies, taxonomies, and knowledge graphs to support interconnected data strategies within S&P Global's Enterprise Data Organization.
Candidates will design scalable data engineering solutions and provide thought leadership to ensure the successful execution of next-generation data architecture initiatives.
The position offers a collaborative culture focused on innovation, flexibility, and delivering outsized impact through advanced semantic technologies.
Matching Summary
Match Score: 75
The role involves developing and enhancing ontologies, taxonomies, and knowledge graphs to support interconnected data strategies within S&P Global's Enterprise Data Organization.
Salary
Not specified; Not specified; Competitive pay and comprehensive benefits including health care and retirement planning
Skills & Requirements
Must-have
Ontology development experience
Semantic web technologies RDF OWL SPARQL
Knowledge graph construction and maintenance
Linked metadata schemes implementation
Enterprise data integration in multi-system environments
Nice-to-have
Cloud services experience AWS Google Cloud Azure
Agile development life cycle understanding
Strong communication with non-technical stakeholders
Influence on strategic semantic vision and roadmap
Participation in governance bodies
Key Requirements
5+ years experience with ontology development
3+ years experience in advanced data integration
Advanced studies in computer science or related field preferred
Programming skills in Python Java or JavaScript
Understanding of data governance and metadata management