Senior Ontology & Data Engineer (Semantic Layer)

Position Summary

We are seeking a highly skilled Senior Ontology & Data Engineer to design, build, and operationalize enterprise semantic and data foundations that power Analytics, AI, Agentic AI, Knowledge Management, and Digital solutions.

The role will be responsible for developing ontologies, taxonomies, semantic models, knowledge graphs, and scalable data pipelines while ensuring data quality, governance, interoperability, and AI readiness across the enterprise.

This position sits at the intersection of Data Architecture, Knowledge Engineering, Data Engineering, and AI Enablement. The role will work closely with business stakeholders, Data Architects, Solution Architects, Data Governance teams, AI Engineers, and Platform teams. The position aligns closely with Semantic Layer and Knowledge Layer initiatives involving ontology development, mappings, graph technologies, metadata management, and pipeline engineering.

Key Responsibilities

Ontology & Semantic Modeling

Design, develop, and maintain enterprise ontologies, taxonomies, controlled vocabularies, and semantic models.

Define business entities, relationships, attributes, hierarchies, and knowledge structures.

Create and maintain enterprise knowledge graphs and semantic layers.

Map source systems and business concepts into canonical semantic models.

Collaborate with business stakeholders to define competency questions, business terminology, metadata standards, and semantic relationships.

Ensure semantic consistency across data products, AI applications, analytics solutions, and enterprise platforms.

Support graph-based querying, reasoning, lineage,



and explainability capabilities.

Data Engineering & Pipeline Development

Design, develop, and maintain scalable data ingestion, integration, transformation, and enrichment pipelines.

Build and optimize ELT/ETL frameworks supporting structured, semi-structured, and unstructured data.

Develop source connectors and integration pipelines for enterprise applications and external sources.

Implement graph-loading processes and semantic data transformations.

Design reusable data models and curated datasets for analytics and AI use cases.

Automate data quality validation, monitoring, lineage, and observability processes.

Support API-based integrations and event-driven architectures.

Semantic Layer & Knowledge Graph Engineering

Implement semantic layer architectures supporting AI and enterprise analytics.

Configure and manage graph databases and semantic repositories.

Develop mappings between physical data assets and business concepts.

Support RAG (Retrieval-Augmented Generation), Knowledge Graph, and Agentic AI solutions.

Optimize semantic search, entity resolution, and knowledge discovery capabilities.

Governance & Standards

Define and maintain ontology governance processes.

Ensure compliance with enterprise data governance policies and standards.





Manage metadata, classifications, business glossary, and semantic versioning.

Establish best practices for semantic modeling and data engineering.

Collaborate with Data Stewards and Data Owners to ensure high-quality trusted data assets.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, Engineering, or related field.

5+ years of experience in Data Engineering, Data Architecture, Knowledge Engineering, or Semantic Technologies.

Experience developing enterprise-scale ETL/ELT pipelines.

Robust SQL and Python programming skills.

Experience with Databricks, Spark, Azure Data Factory, AWS, or similar platforms.

Experience with metadata management and semantic modeling.

Strong understanding of data integration, data quality, lineage, and governance principles.

Preferred Qualifications

Semantic Technologies

RDF

OWL

SPARQL

SKOS

SHACL

Knowledge Graphs

Ontology Engineering

Data Platforms

Databricks

Snowflake

Azure Data Platform

AWS Data Services

Neptune

Neo4j

AI & Analytics

Semantic Layer platforms

RAG architectures

Vector Databases

LLM integrations

Agentic AI architectures

AI-ready data foundations

Success Measures

High-quality enterprise ontology and semantic layer adoption.

Reusable semantic assets across business domains.

Increased semantic consistency and data discoverability.

Reduced effort for onboarding new AI and Analytics use cases.

Improved data quality, lineage, and governance.

Faster delivery of AI, Analytics, and Knowledge Management solutions.

📌 Senior Ontology & Data Engineer (Semantic Layer) (India)
🏢 Document IT
📍 India