Semantic Data Architect - Manager

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

Location

Hyderabad

Other locations: Primary

Location

Only

Salary

Competitive

Date: Oct 23, 2026

Job description

Requisition ID: 1735184

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

EY-Consulting - Data and Analytics - Semantic Data Architect - / Semantic Data Architect / Knowledge Graph Architect-Manager

EY's Consulting Services is a unique, industry-focused business unit that provides a broad range of integrated services that leverage deep industry experience with strong functional and technical capabilities and product knowledge. EY's financial services practice provides integrated Consulting services to financial institutions and other capital markets participants, including commercial banks, retail banks, investment banks, broker-dealers & asset management firms, and insurance firms from leading Fortune 500 Companies. Within EY's Consulting Practice, Data and Analytics team solves big, complex issues and capitalise on opportunities to deliver better working outcomes that help expand and safeguard the businesses, now and in the future. This way we help create a compelling business case for embedding the right analytical practice at the heart of client's decision-making.

Role

Semantic Data Architect

Experience

• Guide

• Guide / 10+ years Primary Skill Area

• Knowledge Graphs, Semantic Modelling, Enterprise Data Modelling & AI Data Platforms

• 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, Explai