Manager-KDNI

Manager-KDNI

We are seeking an experienced Manager-Data Engineer to design, build, and scale enterprise data platforms leveraging Microsoft Fabric, OneLake, Knowledge Graph technologies, Ontology-driven data modeling, and AI-powered data solutions. The role will focus on creating semantic data foundations that enable GenAI, Agentic AI, Graph RAG, analytics, governance, and enterprise knowledge discovery. The ideal candidate will possess strong expertise in modern data engineering, graph technologies, metadata management, semantic modeling, and cloud-native architectures.

Key responsibilities include:

Data Platform Engineering
• Design and implement scalable data platforms using Microsoft Fabric, OneLake, Lakehouse, Data Factory, Data Engineering, and Data Warehouse capabilities.
• Build and manage end-to-end data pipelines for ingestion, transformation, enrichment, and data quality.
• Create medallion architecture solutions (Bronze, Silver, Gold) for enterprise-scale workloads.
• Develop reusable data products and data services for analytics and AI consumption.
• Implement data lineage, metadata management, cataloging, and governance solutions.

Knowledge Graph & Ontology Engineering
• Design and maintain enterprise ontologies, taxonomies, and semantic models.
• Model business concepts, entities, relationships, and hierarchies within knowledge graphs.
• Develop graph-based data structures for enterprise knowledge management.
• Build semantic layers enabling consistent business definitions across data domains.
• Define graph schemas and ontology lifecycle management processes.
• Support graph database implementations using technologies such as Cosmos DB Gremlin, Neo4j, RDF, OWL, or similar platforms.

AI & Intelligent Data Solutions
• Enable Graph RAG and semantic retrieval architectures for GenAI solutions.
• Create entity extraction, relationship discovery, and knowledge enrichment pipelines.
• Design vectorization, embeddings, and semantic search frameworks.
• Support AI agents with contextual enterprise knowledge and grounding mechanisms.
• Improve explainability, traceability, and trustworthiness of AI-generated responses.

Data Modeling & Architecture
• Design conceptual, logical, semantic, and physical data models.
• Establish enterprise-wide domain models and data standards.
• Define canonical data models and cross-domain entity relationships.
• Architect event-driven, batch, and real-time data processing solutions.
• Optimize large-scale analytical workloads and data storage strategies.

Governance & Metadata Management
• Implement metadata-driven engineering practices.
• Build automated data quality frameworks and monitoring solutions.
• Develop lineage capture and impact analysis mechanisms.
• Establish data governance standards, stewardship workflows, and compliance controls.
• Support regulatory and audit requirements through traceable data architectures.

Platform Operations
• Implement CI/CD pipelines and Infrastructure as Code.
• Automate deployment, testing, monitoring, and observability.
• Optimize platform performance, scalability, reliability, and cost.
• Support DataOps, MLOps, and FinOps practices.
• Monitor operational health of Fabric workloads and graph services.

1) Required Skills
a) Data Engineering
• Strong experience with Microsoft Fabric.
• Fabric Data Engineering.
• Fabric Data Factory.
• Fabric Lakehouse.
• Fabric Data Warehouse.
• OneLake architecture.
• Apache Spark / PySpark.
• Delta Lake.
• SQL and T-SQL.
• Python.

b) Knowledge Graph & Semantic Technologies
• Ontology modeling.
• Taxonomy management.
• Knowledge Graph design.
• Entity relationship modeling.
• Semantic data integration.
• Graph data structures.
• Graph query languages.
• Graph RAG architectures.

c) Cloud & Platform
• Azure Data Services.
• Azure Storage.
• Azure Functions.
• Event-based architectures.
• REST APIs.
• Microservices architecture.
• Infrastructure as Code.

d) Governance & Metadata
• Data Catalog.
• Metadata Management.
• Data Lineage.
• Data Governance.
• Data Quality Frameworks.
• Master Data Management.

2) Preferred Skills
• Cosmos DB Gremlin API.
• Neo4j.
• RDF, OWL, SHACL.
• Microsoft Purview.
• Azure OpenAI.
• Semantic Kernel.
• Copilot Studio.
• AI Foundry.
• Vector Databases.
• LangChain or LlamaIndex.
• Graph Machine Learning.
• Agentic AI Architecture.

3) Experience
a) Essential
• 8+ years of Data Engineering experience.
• 3+ years building cloud-native enterprise data platforms.
• Experience delivering large-scale analytics and AI solutions.
• Experience with Microsoft Fabric or Azure Data Platform ecosystem.

b) Preferred
• Experience implementing knowledge graphs and graph databases.
• Experience building Graph RAG solutions.
• Experience defining enterprise ontologies and semantic models.
• Experience supporting AI and GenAI programs.

4) Education
• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field.
• Microsoft Fabric Data Engineer certification preferred

Experience Level

Mid Level