Brijesh Narayanaswamy

A technology leader with deep expertise in Technology Consulting, Data and AI Strategy, Migration & Modernization, Data Platform Assessments. He has successfully led numerous modernization and migration initiatives—from initial assessment through full-scale implementation—leveraging accelerators and automation to drive efficiency. Brijesh is an expert in Data & Analytics Migration and Modernization, with a strong command of leading cloud hyperscalers including AWS, GCP, Azure, Snowflake, and Databricks. His experience spans traditional data platforms as well as cutting-edge AI solutions, including Agentic AI systems that enable autonomous decision-making and orchestration. Known for being self-driven and a fast learner, have consistently delivers impactful, scalable solutions in complex enterprise environments.

• Infosys Lex Certification – AI First
• Scrum
• Python
• Snowflake
• Databricks
• Deeplearning.ai Certification – AI Agents
• Udemy Agentic AI Certifications
• Databricks Data Engineering Training certifications
• Informatica and OBIEE certifications

• Lead Solution Architect | Enterprise Data Modernization Program, Fortune 50 Global Food & Beverage Leader, 11/01/15, Present, Spearheaded the end-to-end architectural roadmap and execution strategy to migrate 74 complex enterprise applications from legacy Teradata to a modern Databricks Lakehouse environment, establishing a comprehensive decommissioning plan for the Teradata footprint., Architected migration pipelines for data ecosystems comprising hundreds of terabytes, systematically mapping, transforming, and validating 100+ tables per application while ensuring zero data loss and minimal business disruption., Successfully decoupled and re-platformed highly diverse downstream consumption layers, ensuring seamless integration and optimized query performance across BI & Reporting: Power BI, OBIEE, Business Objects (BO), and Crystal Reports., Leveraged Databricks’ Delta Lake capabilities to optimize data storage and compute, drastically reducing query latency for downstream BI tools compared to legacy Teradata environments., Partnered with cross-functional business units and application owners to manage the migration lifecycle, align ingestion patterns, and drive post-migration user adoption.
• Global Automotive Manufacturer, As Solution Architect, designed the next-generation data architecture and framework for end-to-end data processing from diverse sources on AWS, utilizing Redshift and Apache Iceberg.
• Leading Cloud Platform and ISP in the US, Provided technology consulting and future-state architecture strategy, including modernization and re-architecture to support migration from AWS to GCP.
• Major US Health Insurance and Services Company, Led strategic technology consulting and assessment to consolidate and modernize multiple data assets. Identified overlaps, defined shared transformation paths, and developed a cost-benefit analysis (CBA) model. The modernization involved migration from Teradata to Databricks.
• Leading US Automotive Manufacturer, Delivered strategic consulting for a major auto OEM with petabytes of data across on-prem systems. Designed a global data platform architecture to support enterprise-wide analytics.
• Global Auto Finance Division of a Major Automotive Manufacturer, Served as Solution Architect for a digital transformation initiative, including core receivables migration from DB2 to Snowflake on AWS. The migration followed a framework-driven, hybrid approach.
• Leading US Manufacturer of Construction & Mining Equipment, Conducted a data integration assessment and designed end-to-end integration patterns for global procurement harmonization.
• Captive Auto Finance Provider in the US, Led assessment and implementation to migrate and modernize the Auto Finance Data Platform from on-premise to a multi-cloud architecture.
• Global Parts Supply Chain for Leading Auto Manufacturer, Established a global Enterprise Data Warehouse (EDW) for a major OEM, integrating data from multiple legal entities. The EDW included diverse datasets such as Order Fulfillment, Procurement, Inventory, WMS, Demand, and Forecasting.