Data Engineer Lead / Architect (Microsoft Fabric & Ontologies)

Target Profile: Senior consultants, architects, or lead engineers with demonstrated expertise in Microsoft Fabric, data engineering, and semantic technologies, capable of driving enterprise-scale implementations with minimal supervision.

Role Overview

• We are seeking senior-level professionals with strong hands-on experience in Microsoft Fabric, Python, and Semantic Data Modeling/Ontologies. The ideal candidate will have proven expertise in designing scalable data platforms, building semantic layers, and enabling business insights through modern data engineering practices.

Key Responsibilities

• Design, develop, and optimize data solutions using Microsoft Fabric.
• Build and manage data pipelines, lakehouses, warehouses, and analytics workloads.
• Develop semantic models, ontologies, and knowledge representations to support business use cases.
• Implement data engineering solutions using Python and modern data frameworks.
• Collaborate with business and technical stakeholders to translate requirements into scalable data architectures.
• Ensure data quality, governance, metadata management, and semantic consistency across data assets.
• Support advanced analytics, AI/ML, and enterprise data initiatives.

Required Skills

• 8+ years of experience in Data Engineering and Analytics.
• Strong hands-on expertise in Microsoft Fabric (Data Factory, Lakehouse, Warehouse, Power BI, OneLake).
• Experience with IQ/Ontologies, Semantic Modeling, Knowledge Graphs, or related semantic technologies.
• Advanced Python programming skills for data processing and automation.
• Strong understanding of data architecture, ETL/ELT, data modeling, and cloud-native analytics platforms.
• Experience working directly with business stakeholders and enterprise-scale data ecosystems.

Preferred Qualifications

• Experience with knowledge graphs, metadata management, and semantic search solutions.
• Exposure to AI/GenAI-enabled data platforms.
• Microsoft Fabric and Azure certifications preferred.