To qualify for the role, you must have
- Relevant experience guide: Guide / 5-10 years
- 4-10 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 up-to-date 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.

📌 Semantic Data Engineer (Hyderabad)
🏢 EY
📍 Hyderabad