Data Architect (Knowledge Graph & Data Governance)

Responsibilities

• Knowledge Graph Engineering: Architect, design, and deploy enterprise Knowledge Graph solutions using Property Graphs and semantic technologies.
• Semantic Modeling: Build, maintain, and extend ontologies, taxonomies, metadata models, and enterprise knowledge structures tailored to client domain requirements.
• Data Governance & Metadata Management: Establish integrated data governance frameworks, including end-to-end data lineage, automated metadata management, business glossaries, data stewardship workflows, and data quality standards.
• Cross-Functional Collaboration: Partner closely with business stakeholders, AI/ML engineers, and data platforms teams to translate complex business domains into scalable semantic models.
• Technical Integration: Integrate knowledge graphs into broader enterprise data fabrics, data meshes, and AI systems (e.g., Graph-RAG, LLM context engines).
• Delivery & Leadership: Act as a technical authority on customer deployments, translating ambiguous client needs into clean, scalable data architecture blueprints.

Requirements

• Knowledge Graphs: Proven experience designing and delivering enterprise Knowledge Graph systems in production.
• Semantic Foundations: Deep expertise in ontology modeling, taxonomy development, metadata architectures, and Property Graph models (e.g., Neo4j, Amazon Neptune, Cosmos DB).
• Governance Expertise: Strong track record in enterprise Data Architecture and Governance, including hands-on experience with metadata lineage, data quality platforms, stewardship frameworks, and enterprise business glossaries.
• Cross-Domain Execution: Demonstrated ability to bridge technical AI/engineering teams and non-technical business stakeholders to drive consensus on data models.
• Technical Stack: Proficiency with graph query languages (Cypher, Gremlin, or SPARQL), graph databases, and modern data stack integration pattern (ETL/ELT pipelines, APIs, and cloud platforms).

Benefits