Sr GDS Consulting - AI And DATA -Semantic Data Engineer

Job Summary

Semantic Data Engineer - Senior Experience Guide

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

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