• 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

• Lead client discovery sessions to gather business and technical requirements, define project scope, and manage stakeholder expectations throughout engagements. • Provide strategic guidance on Adobe Experience Platform adoption, enterprise data strategy, and solution architecture. • Partner with clients to assess data maturity and recommend scalable architecture solutions aligned with business objectives. • Serve as a trusted advisor by translating complex technical concepts into actionable recommendations for both technical and non-technical stakeholders. • Review and validate enterprise data architectures, XDM schema designs, identity resolution strategies, activation plans, and technical specifications. • Provide recommendations across the entire data lifecycle, including data ingestion, ETL/ELT processes, transformation, governance, quality, archival, and lifecycle management. • Guide customer identity mapping and Customer 360 initiatives through effective SQL and NoSQL data modeling strategies. • Promote enterprise data governance standards and repeatable best practices across Adobe Experience Platform implementations. • Review technical design documents and architecture specifications to ensure alignment with Adobe best practices and enterprise standards. • Mentor Data Architects and cross-functional engineering teams by providing architectural oversight and technical leadership. • Collaborate with software engineering consultants, data scientists, and delivery teams to guide scalable data workflows and machine learning operationalization. • Act as the senior data architecture advisor across multiple client engagements, ensuring technical excellence throughout project delivery. • Successfully manage multiple client engagements while balancing competing priorities and maintaining project governance. • Contribute reusable templates, playbooks, frameworks, and Center of Excellence initiatives that strengthen the Adobe consulting practice. • Develop client-facing documentation, presentations, architecture recommendations, and implementation guidance. • Maintain accurate project documentation, workload estimates, and project tracking throughout engagements. • Stay current on Adobe Experience Platform capabilities, Azure cloud technologies, and emerging enterprise data architecture trends. • Identify opportunities to improve delivery methodologies and recommend innovative solutions that address evolving client needs. • Continuously expand technical expertise while contributing knowledge and best practices across the consulting organization.