Sampath Vuthur

Worked on building a scalable data platform to support high-frequency financial transactions, real-time risk assessment, and regulatory compliance. Developed robust data pipelines to integrate transaction data from various sources using tools like Informatica PowerCenter, enabling accurate and timely financial reporting. Leveraged Snowflake for high-performance data warehousing, Databricks for advanced analytics, and Hadoop for large-scale data processing. Focused on enhancing data quality, reducing processing times, and improving data accessibility for critical business decision-making.

Contributed to modernizing Caterpillar’s data infrastructure to support connected equipment, leasing operations, and financial insights across global markets. Led initiatives around cloud migration and unified analytics, integrating high-volume IoT and transactional data into centralized reporting systems. Used Azure Synapse, Snowflake, and Databricks to drive real-time equipment diagnostics, lease portfolio intelligence, and asset lifecycle forecasting. Also supported ML integrations for risk mitigation and operational cost control.

Enhanced Python-based ML models for finance on GCP, achieving a 20% increase in investment accuracy with BigQuery and AI Platform, demonstrating prowess in solving complex business challenges through analytics.

Contributed to Best Buy’s digital analytics platform by designing scalable data pipelines and streaming frameworks on AWS, with deep integration into Snowflake and Databricks for advanced analytics. Built a unified customer data layer by ingesting transactional and behavioral data from e-commerce platforms and in-store systems. Leveraged AWS Glue, S3, and Kinesis for ingestion, while using Databricks for distributed transformation and Snowflake as the enterprise-wide analytical data warehouse to host curated, consumption-ready datasets for reporting, segmentation, and business forecasting. Focused on optimizing marketing attribution, conversion analysis, and supply chain visibility across business teams.

Worked as part of a cross-functional team on the migration of Honeywell’s Azure-based data architecture to a cloud-native AWS platform. The initiative focused on consolidating batch and real-time data from IoT devices, ERP systems, and external APIs into a unified AWS data ecosystem. Contributed to building and replatforming ingestion pipelines using Databricks and Snowflake, supporting the modernization of data workflows and enabling scalable, reliable analytics operations in the new environment.

Assisted in extracting and cleaning operational data using SQL and Excel, improving consistency in weekly sales reports and reducing manual reconciliation across business units. Created basic stored procedures in SQL Server for recurring summaries and performance reports, gaining hands-on experience with joins, filters, and query optimization.