EY - GDS Consulting - AI And DATA -Semantic Data Engineer- Senior

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 Engineer - Senior 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 Engineer - Senior Experience Guide

• Guide / 5-10 years – Primary Skill Area
• Semantic Data Engineering, Knowledge Graphs, Metadata Engineering, AI-Ready Data Products & GraphRAG Enablement

The opportunity Design, build and operationalise semantic data engineering solutions that enable Knowledge Graphs, semantic layers, enterprise search, AI-ready data products, GraphRAG and intelligent data discovery. This role is implementation-focused and bridges data engineering, semantic modelling, metadata engineering and AI-ready data platform delivery. The candidate must be able to engineer semantic models, integrate structured and unstructured data, build semantic data pipelines, support ontology and knowledge graph implementation, and operationalize governed semantic assets across modern lakehouse and enterprise data platforms.

Your Key Responsibilities

• Semantic Data Engineering & Semantic Modelling
• Design and implement semantic data engineering solutions using semantic modelling, ontology implementation, taxonomies, SKOS concepts and governed semantic layer patterns.
• Translate business concepts and architecture guidance into implementable semantic data structures, reusable semantic mappings and operational data products.
• Build semantic models that connect business glossaries, metadata, data lineage, data quality rules and downstream analytics or AI consumption needs.
• Support semantic standards adoption by documenting implementation patterns, naming conventions, mappings and reusable engineering assets.

Data Modelling & Analytical Data Structures

• Apply data modelling practices including conceptual data modelling, logical data modelling, physical data modelling and dimensional modelling for semantic and analytical use cases.
• Implement entity, relationship, hierarchy, classification, metric and attribute structures that support relational, lakehouse, graph and semantic layer consumption.
• Support star schema, snowflake schema, domain model, canonical model and Power BI Semantic Model implementation where required for analytical consumption.
• Partner with data architects and semantic architects to ensure data models are reusable, governed, explainable and aligned with enterprise information architecture.

Knowledge Graph Engineering

• Build and maintain Knowledge Graph solutions using graph data modelling, graph relationships, entity resolution patterns, classifications and hierarchical structures.
• Implement semantic graph pipelines using RDF, OWL, SPARQL and knowledge graph platforms such as Neo4j, Amazon Neptune and GraphDB.
• Integrate structured, semi-structured and unstructured data into semantic ecosystems and knowledge graph foundations.
• Support GraphRAG, semantic search, enterprise search, vector search and AI-driven knowledge retrieval use cases.

Data Integration, ETL/ELT & Lakehouse Engineering

• Develop data integration pipelines using SQL, Python, PySpark, ETL/ELT patterns, APIs, enterprise data integration and metadata-driven engineering patterns.
• Build semantic data pipelines across lakehouse architecture, data platforms, s…