Senior Graph Database/Semantic Data Architect

Our customer is building a next-generation AI platform that enables organizations to securely develop, govern, and operationalize artificial intelligence while ensuring that sensitive data and organizational knowledge remain fully under their control. The platform combines advanced AI capabilities with enterprise-grade governance, security, and data sovereignty to support mission-critical decision-making.

The solution serves government organizations and enterprise customers operating in highly regulated and security-sensitive environments, where reliability, accountability, and trust are essential. The platform supports intelligent decision-making across strategic planning, workforce intelligence, and organizational operations, helping customers leverage AI without compromising security, compliance, or control over their data.

REQUIREMENTS

8+ years of experience in Data Architecture or Enterprise Data Engineering
3+ years of hands-on experience designing Graph Database solutions
Strong experience with Neo4j, Amazon Neptune, TigerGraph, Ontotext GraphDB or similar graph technologies
Deep understanding of RDF, OWL, SPARQL, property graph models, semantic web technologies, and ontology design
Experience designing enterprise Knowledge Graphs or Semantic Data Models
Strong SQL skills and experience with relational databases
Experience integrating graph databases with modern data platforms and cloud environments (AWS, Azure or GCP)
Knowledge of Digital Twin concepts is considered a strong advantage
Experience collaborating with AI/ML teams
Excellent stakeholder communication and architecture documentation skills
Upper-Intermediate or higher level of English

RESPONSIBILITIES

• Design enterprise graph database architecture and semantic data models
• Define ontology strategy and knowledge graph structure
• Lead architecture decisions around graph technologies and semantic data management
• Collaborate with AI engineers to support LLM and reasoning use cases
• Design scalable data integration patterns across structured and unstructured data
• Define best practices for graph data modeling, governance, and performance
• Support client discovery sessions and technical workshops
• Mentor engineering teams on graph technologies
• Produce architecture documentation and technical roadmaps