Ontology Architect

Responsibilities

• Ontology & Taxonomy Design: Lead the creation, modeling, and evolution of enterprise-wide ontologies, taxonomies, and controlled vocabularies that accurately represent complex business domains.
• Knowledge Graph Architecture: Design and implement scalable architecture, ingestion pipelines, and governance for enterprise Knowledge Graphs (Triple Stores or Property Graphs).
• Semantic Layer Strategy: Build and maintain the enterprise semantic layer to abstract physical data complexities, providing a unified, machine-readable business view of data.
• Data Product Augmentation: Partner with domain data teams to map, link, and augment decentralized Data Products using the central ontology to ensure semantic interoperability across the organization.
• Inference & Reasoning: Implement semantic reasoning and inference rules to automatically generate new metadata and uncover hidden insights within the graph.
• Governance & Standards: Establish best practices, version control mechanisms, and data contracts for semantic models, ensuring consistent graph schema updates across business units.

Requirements

• Semantic Standards: Expert-level mastery of core semantic technologies.
• Knowledge Graph Engineering: Hands-on experience designing and operating production-grade Graph Databases / Triple Stores (e.g., GraphDB, Stardog, Amazon Neptune, AllegroGraph, or Neo4j).
• Ontology Modeling Tools: Proficiency with industry-standard ontology engineering and taxonomy management software (e.g., Protégé, TopBraid Composer, PoolParty).
• Modern Data Frameworks: Clear, practical understanding of Data Mesh paradigms, specifically how to design a semantic layer that overlays federated, domain-driven Data Products.
• Traditional Data Modeling: Strong baseline in classic data concepts, including relational databases, dimensional modeling, and ETL/ELT integration patterns.

Preferred Skills

• Data Quality & Validation: Hands-on experience to enforce data quality and constraint validation across graph structures.
• Advanced AI & Graph Analytics: Familiarity with graph algorithms, graph machine learning (GNNs), or leveraging Knowledge Graphs to enhance Large Language Model architectures via Graph RAG (Retrieval-Aug Generation).