Madhanmohan Dunnuthala

About Madhanmohan Dunnuthala

Technical Architect with 18+ years of experience in data integration, enterprise data warehousing, and analytics across Banking, Manufacturing, Automotive, and Finance domains. Specialized in designing scalable, cloud-native data platforms leveraging Power BI, Synapse, and Snowflake. Expert in dimensional modeling, Lakehouse architectures, and modernizing ETL/ELT pipelines. Demonstrated success in transforming legacy regulatory and operational reporting ecosystems through Microsoft Fabric, Snowflake, and Power BI, with a strong focus on performance optimization, governance, and enterprise-scale analytics• End-to-End Architecture: Designed and implemented enterprise-scale analytics solutions using Power BI, building optimized data models, integrating multiple data sources and enabling centralized reporting, governance and self-service BI across the organization.• Cloud Data Integration (Snowflake):o Strategy and execution of migrating the entire data warehouse infrastructure from on-premise (Oracle, SQL Server and Other legacy systems) to Snowflake Cloud Data Platformo Define the overall structure of data warehouses in Snowflake, including schemas, data models (Star, Snowflake and Parent Child hierarchical Models).• Hybrid Data Platforms: Architected hybrid data ecosystems integrating enterprise warehouses (Snowflake, Teradata, SQL Server, Oracle) with Microsoft Fabric Lakehouse (Delta Tables) to support large-scale structured, semi-structured and unstructured data workloads• Power BI: Led end-to-end migration of legacy Cognos, OBIEE and Tableau reporting ecosystems to a unified Power BI analytics platform• Data Ingestion & ETL/ELT Modernization: Large-scale ingestion and transformation pipelines across Snowflake and Microsoft Fabric using Data Factory, Dataflows Gen2, and PySpark notebooks• Power Apps: Designed and implemented the migration of a Cognos TM1 external application to a Power Apps Dataverse architecture, replacing TM1 rules and processes with Power Automate driven workflows.• Data Modeling & Semantic Layer: Built optimized star/snowflake models and enterprise semantic layers in Power BI with calculation groups, KPI’s and hierarchies, ensuring standardized business logic.• Orchestration Tools: Microsoft Fabric, Power BI (Dataflows, DataMart’s, Semantic Models), Azure Data Factory, Databricks and SQL Server Integration Services (SSIS).• Incremental Loads & SCD Strategies: Implemented incremental load, partitioning, and Slowly Changing Dimensions (SCD) patterns natively in Fabric pipelines and Spark-based transformations.

Experience

1. Power Bi Snowflake Fabric Architect

   Wells Fargo

   Feb 2025 — Present · Charlotte, NC, US

   Led banking regulatory reporting transformation by migrating legacy IBM Cognos TM1 solutions to a Microsoft Power BI platform, enhancing compliance, transparency, and data accuracy.• Led the assessment of legacy BI platforms including Cognos and Tableau rationalized reporting inventories and re-engineered data models and SQL logic into standardized, governed Power BI semantic models and dataflows.• Built robust file and data upload functionalities using Power Apps, Power Automate, and Dataverse to streamline business submissions and automate validations• Reengineered traditional ETL processes into Fabric Lakehouse based ELT pipelines and Power BI Dataflows, improving scalability, performance, and cost efficiency.• Automated ETL and reporting workflows by replacing legacy batch processes with cloud-native orchestration tools(Fabric pipeline and Dataflows)• Designed and optimized dimensional models, star/snowflake schemas and fact/dimension tables for reporting and analytics.• Transformed complex Cognos reports into optimized Power BI dashboards, leveraging star schema data models, semantic layers, and DirectQuery/Direct Lake connections• Designed and delivered end-to-end Power Platform solutions using Power Apps, Power Automate, Dataverse, and Microsoft Fabric Lakehouse, integrating automated pipelines for data ingestion, transformation and reporting• Optimized performance through incremental refresh, aggregation tables, partitioning strategies, and query folding to handle large historical and forecast datasets

Education

• Jawaharlal Nehru Technological University

  Bachelor of Technology (B.Tech.), Computer Science