• 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 optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture. • Lead the 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.

• 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 optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture. • Lead the 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.

• Design and evolve data platforms using Lakehouse architecture • Develop scalable pipelines • Define architecture, governance, and performance standards with Databricks, Delta Lake, and PySpark • Implement solutions on Databricks and Delta Lake • Support technical teams and document solutions

• Design, build, and operate modern, end-to-end, cloud-native data warehouses • Develop and maintain robust ELT/ETL pipelines that integrate multiple internal systems (servicing, origination, CRM) and third-party data feeds • Create scalable data models for reporting, BI (semantic layer), and future AI/ML initiatives • Implement data quality, governance, and security practices aligned with stringent U.S. financial regulations • Work closely with C-suite stakeholders and directors in the U.S. to translate business requirements into technical data architecture • Optimize the performance of queries, pipelines, and workflows to ensure efficiency and cost control