Job Summary
Semantic Data Engineer - Senior Experience Guide
The prospect
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 arch
📌 Sr GDS Consulting (Hyderabad)
🏢 EY
📍 Hyderabad