EY - GDS Consulting - AI And DATA -Semantic Data Architect- Manager

The opportunity

Lead the design and implementation of enterprise-scale semantic data platforms that enable advanced analytics, AI, Knowledge Graphs, intelligent data discovery and governed enterprise data modelling. The role combines strong data modelling, semantic architecture, ontology engineering, knowledge graph design, Microsoft Fabric data platform architecture, metadata governance and AI-ready information architecture. The candidate must be able to bridge business concepts, enterprise data assets, semantic models, knowledge graphs, vector search and LLM-enabled retrieval frameworks.

Your key responsibilities

Semantic Architecture & Knowledge Modelling

• Develop domain ontologies using RDF/OWL, taxonomies, controlled vocabularies, SKOS concept schemes and semantic models to standardise business knowledge representation.
• Collaborate with Business Analysts, Data Architects, domain SMEs and stakeholders to elicit business concepts, align semantic definitions and translate requirements into ontology-driven models.
• Establish semantic governance processes, metadata standards, ontology lifecycle management and reusable semantic modelling patterns.
• Define semantic standards that connect business glossaries, domain concepts, enterprise data assets and AI-ready knowledge structures.

Data Modelling & Enterprise Information Architecture

• Lead conceptual, logical and physical data modelling across enterprise domains, ensuring data entities, relationships, hierarchies, cardinality, keys, constraints and business definitions are clearly captured and governed.
• Create and maintain enterprise data models, canonical data models, domain models, dimensional models and semantic models that align business terminology with analytical, lakehouse, relational and graph-based data structures.
• Translate business requirements into robust information models and data structures that support Microsoft Fabric Semantic Models, Power BI semantic layers, Knowledge Graphs, GraphRAG, enterprise search and AI-ready data products.
• Apply strong modelling disciplines including normalisation, denormalisation, star/snowflake schema design, slowly changing dimensions, reference/master data modelling, metadata modelling and model version management.
• Partner with business SMEs, architecture teams, data stewards and engineering teams to ensure data models are reusable, governed, explainable, lineage-aware and aligned with enterprise data governance standards.

Knowledge Graph Development

• Design and implement enterprise Knowledge Graph solutions that model business entities, relationships, hierarchies, dependencies and contextual knowledge.
• Build semantic layers that support AI, intelligent search, recommendations, GraphRAG architectures and contextual knowledge retrieval.
• Integrate structured, semi-structured and unstructured data into enterprise knowledge graphs.
• Guide graph data modelling patterns for business domains, relationships, hierarchies, dependencies and reusable knowledge assets.

Microsoft Fabric & Data Platform Architecture

• Design enterprise semantic architectures using Microsoft Fabric as the operational platform for data integration, governance, analytics and AI-driven business insights.
• Architect Lakehouse, Data Warehouse, OneLake and Semantic Models within the Microsoft Fabric ecosystem.
• Establish metadata-driven and domain-oriented data architecture patterns that connect lakehouse data products, semantic layers, AI retrieval and governance workflows.
• Partner with data engineering teams to ensure semantic assets are operationalised through reliable, governed and scalable data platform patterns.

AI & Advanced Analytics Enablement

• Enable AI and Generative AI solutions through semantic enrichment, contextualised data models and ontology-driven knowledge assets.
• Support GraphRAG, Retrieval-Augmented Generation, enterprise search, semantic search and intelligent agents.
• Design semantic frameworks that improve LLM grounding, explainability, traceability and enterprise knowledge retrieval.
• Partner with AI engineering teams to ensure semantic structures are AI-ready, governed and usable for enterprise retrieval and decision-support use cases.

Governance & Architecture Leadership

• Define enterprise standards, policies and governance frameworks for ontology management, semantic models, taxonomies and knowledge graph assets using platforms such as Microsoft Purview and Collibra.
• Provide technical leadership across architecture, engineering, AI, governance and business teams.
• Conduct architecture reviews and establish best practices for semantic technologies, enterprise data modelling and AI-ready data ecosystems.
• Mentor architects, engineers and data stewards on semantic modelling, knowledge graph design and governed data modelling approaches.

Skills and attributes for success

Skill / capability area    -    Details

• Semantic technologies  -  RDF, OWL, Ontologies, SKOS, Taxonomies, Controlled Vocabularies, Semantic Web, Semantic Modelling, Ontology Engineering.
• Data modelling   -   Conceptual Data Modelling, Logical Data Modelling, Physical Data Modelling, ER Modelling, Dimensional Modelling, Canonical Models, Domain Models, Data Dictionary, Metadata Modelling.
• Knowledge graphs       -       Knowledge Graphs, Graph Data Modelling, Neo4j, Amazon Neptune, GraphDB, GraphRAG, Semantic Search, Enterprise Search, Relationship Modelling, Hierarchies.
• Data platform architecture     -     Microsoft Fabric, OneLake, Lakehouse, Data Warehouse, Data Factory, Semantic Models, Power BI Semantic Layer, Lakehouse Architecture, AI-ready Data Products.
• AI and advanced analytics    -       GraphRAG, RAG, Vector Search, LLM Grounding, AI Knowledge Retrieval, Intelligent Agents, GenAI Enablement, Explainability, Traceability.
• Governance and metadata    -       Microsoft Purview, Collibra, Metadata Management, Business Glossaries, Data Lineage, Data Quality Rules, Ontology Lifecycle Management, Semantic Governance.

To qualify for the role, you must have

• Relevant experience guide: Guide / 10+ years
• 10+ years of experience in Data Architecture, Data Engineering, Enterprise Information Management or enterprise data modelling-led architecture roles.
• 5+ years of hands-on experience in semantic technologies, knowledge modelling and enterprise data modelling.
• Hands-on experience with Microsoft Fabric, semantic models, Power BI semantic layer, conceptual/logical/physical data modelling, RDF/OWL ontology engineering, Knowledge Graph platforms and governance platforms such as Microsoft Purview or Collibra.
• Preferred experience with data modelling tools such as ER/Studio, Erwin, SAP PowerDesigner, Hackolade or equivalent.

Ideally, you'll also have

• Architect by mindset, with strong ability to connect business concepts, data models, semantic assets and AI-ready knowledge structures.
• Strong stakeholder engagement skills with the ability to work across business SMEs, data architects, data engineers, AI engineers and governance teams.
• Comfortable defining semantic standards, ontology governance, knowledge graph design patterns and enterprise data modelling practices.
• Able to create explainable, lineage-aware and