We are building an AI-native procurement platform where data is no longer treated as a reporting asset, but as the foundation for products, automation, reasoning and intelligent decision-making.

This role defines the enterprise data architecture spanning operational systems, analytical platforms, semantic models, ontologies, knowledge graphs, context graphs and AI-ready data products. You ensure that information is structured once, understood consistently, governed appropriately and reusable across every application, workflow, analytics solution and AI capability.

This is a senior, hands-on architecture role. You work across Product, Platform, Data, AI, Security and Enterprise Architecture to define how information is modelled, stored, linked, governed and consumed throughout the organisation.

Rather than optimizing individual technologies, you architect an information ecosystem that supports today's operational needs while providing the foundation for tomorrow's autonomous agents, semantic reasoning and intelligent automation.

Responsibilities

• Define and evolve the enterprise data architecture across transactional systems, analytical platforms, semantic models and AI platforms.
• Design the overall information architecture spanning:

• • OLTP systems

• • Lakehouse

• • Data Products

• • Semantic Models

• • Ontologies

• • Knowledge Graphs

• • Context Graphs

• • Vector Stores

• • AI Retrieval Architecture

• Define canonical business entities, relationships, taxonomies and domain boundaries across the enterprise.
• Architect how structured, semi-structured and unstructured information is connected into a unified enterprise knowledge model.
• Design how documents, contracts, communications, workflows and business events are transformed into governed enterprise knowledge.
• Define patterns for entity resolution, relationship management, provenance, confidence scoring and temporal modelling.
• Establish modelling standards for transactional, analytical, semantic and graph workloads.
• Design multi-tenant information models supporting isolation while enabling reusable platform capabilities.
• Work closely with the Data Governance Lead to ensure architecture and governance evolve together through ownership models, metadata, lineage, quality controls and automated guardrails.
• Partner with Product, AI and Engineering to ensure new capabilities build upon shared enterprise information assets rather than isolated datasets.
• Guide engineering teams on data modelling decisions, balancing delivery speed with long-term architectural integrity.
• Ensure new data products align with enterprise information architecture and contribute reusable business capabilities.

What you will work with

• Enterprise operational data models
• Lakehouse and medallion architectures
• Data Products
• Canonical domain models
• Business ontologies
• Knowledge Graphs
• Context Graphs
• Document-to-entity pipelines
• Entity resolution
• Relationship extraction
• Temporal modelling
• Metadata and catalog platforms
• Lineage and provenance
• Vector indexes and embedding pipelines
• Retrieval architectures
• Graph traversal and semantic reasoning
• Multi-agent context architectures

Artifacts & deliverables you should expect

• Enterprise Information Architecture
• Canonical enterprise data model
• Domain model definitions
• Enterprise ontology
• Knowledge Graph schema
• Context Graph architecture
• Reference architectures for Data Products
• Information modelling standards
• Data Product design principles
• Entity modelling guidelines
• Relationship modelling standards
• Document ingestion patterns
• Retrieval architecture
• Reference implementations for semantic enrichment
• Architecture Decision Records
• Enterprise modelling roadmap

Requirements

• Strong experience architecting enterprise data platforms across both operational and analytical environments.
• Deep understanding of relational, document, graph and vector data architectures and where each should be applied.
• Experience designing enterprise semantic models, canonical business entities and domain-driven information models.
• Experience defining information architectures that support products, analytics, automation and AI simultaneously.
• Understanding of knowledge graph concepts, ontologies, semantic modelling and enterprise metadata management.
• Strong understanding of data products, domain-oriented architecture and modern lakehouse patterns.
• Experience designing retrieval architectures that combine structured, unstructured and contextual information.
• Understanding of metadata, lineage, provenance, stewardship and governance frameworks.
• Ability to work across Product, Platform, Data, AI, Security and Enterprise Architecture.
• Excellent communication skills in English with the ability to simplify complex architectural decisions for both technical and business audiences.
• A pragmatic mindset that balances architectural integrity with rapid delivery.

How we approach data architecture

• Information is a platform capability, not an implementation detail.
• Business meaning is defined once and reused everywhere.
• Relationships are as important as individual data.
• Knowledge is modelled explicitly, not recreated inside applications.
• Data products inherit enterprise semantics rather than defining their own.
• Governance should be automated wherever possible.
• Architectures should evolve incrementally while remaining internally coherent.

Why this role matters

Our competitive advantage is built upon a shared enterprise understanding of procurement, suppliers, contracts, obligations, risks, workflows and decisions.

Without a coherent information architecture, every product, AI capability and analytical solution will gradually diverge, increasing complexity, reducing trust and limiting reuse.

This role ensures the organisation develops a unified enterprise knowledge foundation that enables consistent products, scalable automation, explainable AI, reusable data products and intelligent agent orchestration across the platform.

I would make one structural distinction from your current thinking:

• Enterprise Architect owns the architecture of the systems (applications, platforms, integrations, security boundaries, operating model).

• Data Architect owns the architecture of the information (data, semantics, ontologies, knowledge graphs, context graphs, retrieval, data products, AI-ready knowledge).

Those two roles should be peers and work very closely together. Given the importance of knowledge graphs and context graphs to your platform, I would consider the Data Architect one of the three or four most strategically important technical hires in the entire organisation.