Data Architect

Confidential; Lisboa; Presencial; EUR 95 000 - 140 000; Tempo integral

Resumo da oferta

Confidential is building an AI-native procurement platform and seeks a senior data architect to define enterprise data architecture spanning transactional and analytical systems, semantic models, ontologies and knowledge graphs. You ensure information is structured once, governed properly and reusable across applications and AI capabilities.

This hands-on role collaborates with Product, Platform, Data, AI, Security and Enterprise Architecture to build an information ecosystem that supports

Qualificações

• 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.

Responsabilidades

• 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 and Context Graphs.
• 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 and workflows 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 with Data Governance Lead to evolve governance with ownership models, metadata, lineage, quality controls and guardrails.
• Partner with Product, AI and Engineering to ensure new capabilities build upon shared enterprise information assets.
• Guide engineering teams on data modelling decisions balancing speed and architectural integrity.
• Ensure new data products align with enterprise information architecture and reusable business capabilities.

Conhecimentos

Data architecture

Graph databases

Semantic models

Metadata governance

English communication

Formação académica

Bachelor's degree in a relevant field

Descrição da oferta de emprego

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 organization.

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
• Metadata and catalog platforms
• Lineage and provenance
• Vector indexes and embedding pipelines
• Graph traversal and semantic reasoning

Artifacts & deliverables you should expect

• Canonical enterprise data model
• Context Graph architecture
• Reference architectures for Data Products
• Entity modelling guidelines
• Reference implementations for semantic enrichment

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