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, semantic layers, knowledge graph platforms and AI-ready data products.
Engineer reliable data flows for business glossaries, metadata management, lineage capture, semantic mappings, graph loading and data quality validation.
Collaborate with platform teams to implement scalable and maintainable semantic data products across lakehouse, warehouse and graph ecosystems.
AI-Ready Data Products, GraphRAG & LLM Grounding
Prepare AI-ready data products that improve LLM grounding, contextual retrieval, semantic search and explainability for AI and GenAI use cases.
Support GraphRAG, RAG, vector search, semantic search, enterprise search and intelligent agent patterns using trusted semantic data foundations.
Partner with AI engineering teams to ensure semantic data assets are usable for retrieval, contextual reasoning and AI-enabled data discovery.
Contribute to agentic AI patterns where semantic metadata, knowledge graphs and governed data products support autonomous discovery or insights workflows.
Governance, DevOps, Data SRE & Architecture Support
Implement metadata management, data governance, data lineage, data quality, access controls and semantic governance integrations using tools such as Microsoft Purview, Unity Catalog and Immuta.
Apply Git, CI/CD, DevOps, documentation, testing and release practices for semantic data pipelines, ontology assets, graph mappings and generated artefacts.
Support Data SRE and observability practices for semantic pipelines, graph loads, data quality checks, lineage jobs, API integrations and AI-ready data products.
Provide architecture support, stakeholder collaboration and technical documentation across semantic architecture, data engineering, governance and AI teams.
Skills And Attributes For Success
Skill / capability area - Details
Semantic engineering - Semantic Data Engineering, Semantic Modelling, Semantic Layers, RDF, OWL, SPARQL, Ontologies, Taxonomies, SKOS, Business Glossaries, Metadata Engineering.
Data modelling - Data Modelling, Conceptual Data Modelling, Logical Data Modelling, Physical Data Modelling, Dimensional Modelling, Graph Data Modelling, Power BI Semantic Models.
Data engineering -Data Engineering, SQL, Python, PySpark, Data Integration, ETL/ELT, APIs, Enterprise Data Integration, Data Pipelines, Lakehouse Architecture.
Knowledge graph platforms - Knowledge Graphs, Neo4j, Amazon Neptune, GraphDB, Graph Data Modelling, Semantic Search, Enterprise Search, GraphRAG, Vector Search.
Governance and metadata - Metadata Management, Data Governance, Data Lineage, Microsoft Purview, Unity Catalog, Immuta, Data Quality, Documentation, Architecture Support.
AI and operations - AI-Ready Data Products, LLM Grounding, GenAI, Agentic AI, Git, CI/CD, DevOps, Data SRE, Observability, Stakeholder Management.
To qualify for the role, you must have
Relevant experience guide: Guide / 5-10 years
5-10 years of experience in Data Engineering, Data Management, Information Architecture, Semantic Data Engineering or AI-ready data platform delivery.
2-4+ years of hands-on experience with semantic technologies, ontology implementation, knowledge graphs, graph modelling or semantic data platforms.
Strong implementation experience with SQL, Python, PySpark, RDF, OWL, SPARQL, semantic models, metadata engineering, data pipelines and modern lakehouse architecture.
Preferred experience with Neo4j, Amazon Neptune, GraphDB, Microsoft Purview, Unity Catalog, Immuta, Power BI Semantic Models, GraphRAG, vector search and enterprise AI solutions.
Ideally, you'll also have
Implementation-focused engineer who can operationalise semantic architecture into reusable pipelines, semantic models, metadata assets and knowledge graph solutions.
Strong collaboration skills across semantic architects, data architects, data engineers, governance teams, stakeholders and AI engineering teams.
Comfortable building governed, lineage-aware, observable and AI-ready semantic data products for enterprise search, GraphRAG and self-service analytics.
Able to document implementation patterns, support architecture decisions and improve operational reliability through DevOps, Data SRE and observability practices.
This role is not a traditional data engineer. It is a Semantic Data Engineer who operationalises ontologies, semantic models, metadata pipelines, knowledge graphs and AI-ready data products so enterprise knowledge can be consumed reliably by analytics, search, GraphRAG and agentic AI systems.
What We Look For
Implementation-focused engineer who can operationalise semantic architecture into reusable pipelines, semantic models, metadata assets and knowledge graph solutions.
Strong collaboration sk

Work arrangement
No