Data Architect IV: Enterprise Graph & AI Platforms

Noblis; Chantilly (VA); On-site; USD 146,000 - 228,000; Full time

Job summary

Noblis is seeking a Data Architect IV in Chantilly, VA, to design and govern an enterprise-scale data and intelligence layer. You will bridge traditional data infrastructure with graph-based semantic modeling to power AI reasoning and analytics.

The role requires deep expertise in RDF/OWL/SPARQL, knowledge graphs, distributed systems, and cloud deployments. Collaboration with AI/ML teams is essential for scalable, production-grade graph assets.

Qualifications

• US Citizenship required and TS clearance eligibility.
• 10+ years in data architecture or distributed systems design.
• Expertise in semantic web standards and knowledge graphs.
• Experience with cloud deployments across AWS/Azure/GCP.
• Proven ability to build scalable enterprise data platforms.
• Experience with GraphRAG and LLM-integrations is a plus.

Responsibilities

• Designs and governs an enterprise-scale data and intelligence layer.
• Create knowledge graphs and ontologies using RDF/OWL/SPARQL.
• Architect hybrid retrieval systems combining graph queries and vector search.
• Develop distributed data systems with real-time AI pipelines.
• Ensure data governance, metadata, and secure semantic reuse.
• Collaborate with AI/ML teams to support LLM consumption.
• Integrate semantic tech with cloud and orchestration platforms.

Skills

Data architecture

Graph databases

W3C semantic web

Cloud platforms (AWS/Azure/GCP)

GraphRAG / hybrid retrieval

Knowledge graphs

Education

Bachelor's degree

Master's degree

PhD (preferred)

Tools

Neo4j

Amazon Neptune

TigerGraph

RDF/OWL/SPARQL

Job description

Noblis is seeking a Data Architect IV in Chantilly, VA, to design and govern an enterprise-scale data and intelligence layer. You will bridge traditional data infrastructure with graph-based semantic modeling to power AI reasoning and analytics.

The role requires deep expertise in RDF/OWL/SPARQL, knowledge graphs, distributed systems, and cloud deployments. Collaboration with AI/ML teams is essential for scalable, production-grade graph assets.