Overview
As a Semantic Graph & Ontology Architect, you will play a crucial role in transforming enterprise data into actionable insights by developing a Smart Data Fabric that integrates diverse data sources. You will work closely with cross-functional teams to create scalable graph schemas, design hybrid semantic layers, and implement ontologies that enhance AI capabilities. This position offers the opportunity to lead innovative data architecture projects while promoting semantic consistency and data governance in a fully remote setting.
Responsibilities
- Design scalable graph schemas (LPG and/or RDF/OWL) to meet semantic and inference requirements.
- Author and optimise queries using Cypher, Gremlin, and SPARQL for seamless data traversal and reasoning.
- Create and maintain formal ontologies and taxonomies while managing their versioning and lifecycle.
- Implement logical inference for agent decision-making and ensure workflow integrity.
- Establish standards for semantic consistency and data quality checks.
- Define clean API layers for semantic enrichment and retrieval.
- Set performance budgets to ensure efficient query execution and prevent issues.
- Document standards and mentor engineers in adopting graph and semantic patterns.
Requirements
- Bachelor’s or master’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field.
- 7-12 years of experience in graph databases, semantic modelling, and ontology engineering.
- Expertise in query languages like Cypher, Gremlin, and SPARQL, with a solid understanding of LPG vs RDF/OWL trade-offs.
- Hands-on experience with Neo4j, AWS Neptune, TigerGraph, or Stardog in a production environment.
- Proficient in mapping enterprise data (Snowflake, MongoDB, SharePoint, ERP) into graph and ontology layers.
- Strong knowledge of RBAC/RACI, data governance, lineage, and security controls.
- Familiarity with AWS services (IAM, VPC, S3, EKS/ECS/Lambda).