Knowledge Graph Engineer, Ontologist

Role Overview

• Lead the design and execution of enterprise-scale semantic layers to standardize business meaning and enable trusted analytics, AI, and Agentic use cases.
• Define and operationalize ontologies, context graphs, and knowledge graphs across domains to power reasoning, explainability, and decision intelligence.
• Enable semantic-first AI and Agentic analytics, ensuring LLMs and agents can consume governed business context, metrics, and rules.
• Define canonical semantic vocabularies that standardize meaning across structured and unstructured data sources.
• Drive production-scale execution of semantic and knowledge platforms with strong standards for performance, governance, security, and lifecycle management.
• Evangelize Agentic Data Engineering, driving adoption through patterns, playbooks, and real-world deployments across the enterprise.
• Define and promote standards and best practices for semantic modeling and ontology reuse across delivery teams.
• Partner with architects and engineers to embed semantic models into data products, AI pipelines, and activation layers.
• Work closely with AI Data Architects and AI Data Engineers to operationalize ontologies into production systems (e.g., via graphs, metadata services, APIs).
• Align ontologies with enterprise data governance, lineage, and quality standards.
• Enable explainability by ensuring AI outputs can be traced back to governed semantic definitions.
• Serve as the enterprise authority on semantic engineering and ontology practices.
• Contribute to communities of practice, reference guidance, and internal enablement materials.

Requirements

• 8–12+ years of hands-on experience in semantic layer architecture, ontology modeling, and knowledge graph design at enterprise scale.
• Deep, hands‑on expertise with RDF, OWL (OWL2), RDFS, SKOS, SPARQL (querying, optimization, semantic analytics), and W3C semantic web standards
• Proven experience designing and operating knowledge graphs at enterprise scale
• Hands‑on experience with graph or triple‑store technologies (e.g., Neo4j, Neptune, TigerGraph, Spanner Graph)
• Experience integrating knowledge graphs with LLMs, RAG pipelines, vector stores, and Agentic frameworks.
• Strong understanding of AI consumption patterns, including embeddings, grounding, and explainability
• Experience integrating semantic layers with data platforms, APIs, metadata systems, and AI pipelines
• Ability to translate complex domain knowledge into formal, machine‑readable semantic structures
• Strong understanding of context-aware data engineering and semantic interoperability.
• Proven ability to move from strategy → pilot → scaled enterprise capability.
• Strong executive influence and thought leadership in Agentic analytics and AI‑native data engineering.
• Hands-on experience with AWS, GCP, and Snowflake
• Excellent communication, presentation, and leadership skills.

Tech Stack

• AWS
• Google Cloud Platform
• Neo4j

Benefits

• Other rewards may include short-term or annual bonuses
• Long-term incentives
• On-the-spot recognition