Enterprise Data Architect

The role we are looking for a Data Architect to own the data and semantic foundation of an Agentic Knowledge Graph platform — the Knowledge Graphs, ontology, and ingestion pipelines that the agent layer reasons over. You will work alongside a GenAI / AI Architect (agent orchestration) and a Graph Engineer (Neo4j implementation), and your work must be production-grade and defensible in an enterprise Architecture Review Board setting. Required Skills: Experience designing schemas across multiple connected KG types (Systems, Process, Data, Regulatory) — not siloed graphs. Ability to model ERP three-layer structure (program, database, data) and design extraction and traversal logic across all three. Fluency working alongside a Graph Engineer on Neo4j schema decisions without needing to own the Cypher implementation. Prior involvement in an enterprise architecture review process — you know how to frame data architecture in terms of cost, risk, lineage, and governance. Comfortable with hosting ambiguity: designs that hold across cloud, on-premises, and hybrid until a final hosting decision is made. Production knowledge graph systems — semantic layer and ontology design that has been built, iterated on, and maintained against real enterprise source systems. Enterprise data extraction at scale — on-premises ERP, heterogeneous multi-system estates, and the complexity that comes with real enterprise data. Enough SAP depth to reason about RFC access, ABAP table structure, and the distinction between the program, database, and data layers. ABAP development experience is not required. Ingestion pipeline architecture — extraction, normalisation, transformation, and graph loading with clear lineage at every step. ARB-grade documentation — logical diagrams, integration flows, data governance specs. Not slide-deck architecture. Enterprise cloud or hybrid infrastructure — Azure, Kubernetes, or equivalent. Containerized workload deployment experience expected. #J-18808-Ljbffr MethodHub