Lead Data Engineer (Microsoft Fabric)

About the role

Blend, a leading AI services provider, is seeking a Lead Data Engineer to design and implement ETL pipelines and data transformation processes on Microsoft Fabric, supporting integration of data from over 80 ERP systems.

Key responsibilities

• Conduct comprehensive gap analysis and data mapping across 80+ ERP systems to identify integration challenges, data inconsistencies, and transformation requirements.
• Design and develop standardized ETL pipelines and data transformation processes for migrating enterprise data into Microsoft Fabric.
• Build robust data quality frameworks and validation rules to ensure data readiness for AI and analytics workloads.
• Lead hands‑on implementation of ETL standards and best practices, establishing repeatable patterns and automation scripts for multi‑source integrations.
• Develop and optimise data models that support seamless transformation from fragmented ERP sources into a unified, AI‑ready data architecture.
• Lead pilot implementation phase, testing and refining standards against real ERP data before full‑scale rollout.
• Mentor and guide team members on ETL development, data transformation techniques, and Fabric‑specific engineering practices.
• Collaborate with stakeholders to document data lineage, transformation logic, and integration patterns for knowledge transfer and governance.
• Troubleshoot data inconsistencies and implement corrective measures to maintain data integrity throughout the pipeline.

Required profile

• Advanced proficiency in SQL for complex data transformations, query optimisation and performance tuning.
• Strong Python expertise for automation scripting, data pipeline development and ETL orchestration.
• Hands‑on experience designing and implementing ETL solutions across complex, multi‑source environments.
• Proven experience with enterprise ERP systems and large‑scale data integration scenarios.
• Solid understanding of the Azure ecosystem and practical experience with Microsoft Fabric for data engineering.
• Strong knowledge of data modelling principles and ability to design schemas that support AI and analytics use cases.
• Experience with data quality assessment, validation frameworks and data profiling techniques.
• Excellent problem‑solving skills with focus on scalability, maintainability and performance optimisation.
• Strong communication skills and ability to collaborate with technical and business stakeholders.
• Advanced English language proficiency.

Required skills

• SQL
• Python
• Azure
• Microsoft Fabric
• Data modelling
• ETL development
• Data quality frameworks
• ERP integration