Join EY's GDS Consulting team as a Senior Semantic Data Engineer and shape the future of AI-ready data products and knowledge graphs. This role offers the opportunity to design, build, and operationalize sophisticated semantic data engineering solutions, bridging data engineering, semantic modeling, and AI platform delivery.

You will be instrumental in creating a better working world by helping clients leverage deep industry experience with strong functional and technical capabilities. This position is ideal for an implementation-focused engineer eager to make a significant impact in the financial services sector and beyond.

Design and implement semantic data engineering solutions, including knowledge graphs, semantic layers, and enterprise search capabilities. Engineer semantic models that integrate business glossaries, metadata, and data lineage for AI consumption. Apply data modeling practices to support relational, lakehouse, and graph databases. Build and maintain knowledge graph solutions using RDF, OWL, and SPARQL, integrating diverse data sources. Develop data integration pipelines and semantic data flows across modern data platforms. Prepare AI-ready data products to enhance LLM grounding and contextual retrieval. Implement data governance, metadata management, and DevOps practices for semantic assets.

A minimum of 5-10 years of experience in Data Engineering, Data Management, or Semantic Data Engineering is required. Candidates should possess 2-4+ years of hands-on experience with semantic technologies, ontology implementation, or knowledge graphs. Strong proficiency in SQL, Python, PySpark, RDF, OWL, SPARQL, semantic models, and lakehouse architecture is essential. Experience with graph platforms like Neo4j, Amazon Neptune, or GraphDB, and governance tools such as Microsoft Purview, Unity Catalog, or Immuta is highly preferred. This role demands an implementation-focused engineer capable of operationalizing semantic architecture into robust, AI-ready data products.