Title:  Data Engineer - Semantics & Domain Models

We are building the semantic and graph foundation for an AI-native procurement platform. This role defines and evolves the ontologies, knowledge graphs, and context graphs that allow applications, analytics, workflows, and AI agents to understand procurement data consistently.

You will model the business meaning, entities, relationships, events, and context that sit beneath the platform’s intelligence layer. This is not dashboard semantics. This is the semantic backbone for retrieval, reasoning, agent orchestration, workflow automation, and decision support.

Responsibilities

• Define and maintain procurement ontologies covering entities, attributes, relationships, events, states, and business concepts.
• Own the semantic model for core domains such as suppliers, contracts, obligations, sourcing events, categories, risks, savings, and operational workflows.
• Design and evolve procurement knowledge graphs representing entities, relationships, dependencies, events, and business context.
• Define context graph patterns used to assemble relevant information for users, workflows, retrieval systems, and AI agents.
• Model temporal relationships, lifecycle states, provenance, hierarchy, ownership, and dependency structures explicitly.
• Define graph construction patterns linking structured data, documents, communications, workflow events, and external signals to canonical entities.
• Establish approaches for entity resolution, supplier normalization, deduplication, identity management, and relationship confidence.
• Define validation rules and quality expectations for ontology and graph structures.
• Ensure semantic and graph models support retrieval, reasoning, recommendations, workflow automation, and agentic execution.
• Collaborate with Data Architecture, Data Engineering, Product, AI, Domain, and Governance teams to ensure graph structures are technically feasible, governed, and useful.
• Help eliminate duplicated business logic from dashboards, applications, and workflow tools by moving meaning into shared semantic and graph assets.

What you will work with

• Procurement ontologies
• Knowledge graphs and context graphs
• Entity and relationship models
• Graph-ready data products
• Structured and unstructured data
• Document-to-entity and event-to-entity linking
• Metadata, provenance, lineage, and confidence scoring
• Graph, vector, relational, and retrieval-based architectures
• AI agents, retrieval systems, automation workflows, and analytics consumers

Requirements

• Experience with semantic modelling, ontology design, knowledge graphs, graph data modelling, or domain modelling.
• Strong understanding of entities, relationships, events, lifecycle states, and business context.
• Ability to translate complex procurement concepts into reusable semantic and graph structures.
• Familiarity with graph databases, RDF/property graph concepts, ontologies, taxonomies, or knowledge representation.
• Understanding of entity resolution, normalization, provenance, lineage, and metadata management.
• Ability to design models that support both human interpretation and machine reasoning.
• Comfort collaborating with data engineers, architects, product teams, domain experts, and AI engineers.
• Strong attention to consistency, correctness, and long-term semantic maintainability.

Why this role matters

Knowledge and context graphs are core IP for the platform. They define how the system understands procurement, how agents retrieve context, how workflows reason over relationships, and how intelligence scales beyond one-off datasets or reports.

Without strong semantic and graph foundations, AI workflows become brittle, retrieval becomes unreliable, and automation cannot safely scale. This role ensures that the platform has a durable, governed, and reusable intelligence layer.