Dinesh Kundeti

I am currently contributing to the modernization of CVS Health’s enterprise analytics, focused on transitioning legacy SAS and Teradata based pharmacy (pharma) claims reporting to a cloud-native data platform. The initiative enabled faster access to trusted claims data supporting analytics and machine learning, while reducing cost and dependency on legacy systems. Established a scalable, governed foundation for drug utilization analysis, reimbursement insights, and enterprise reporting.

At Cisco, I contributed to a real-time telemetry analytics platform that transformed how network performance and security data was analyzed across global enterprise environments. The platform enabled near real-time visibility into traffic patterns, anomalies, and operational risks, helping teams proactively maintain SLAs and security posture. Replaced legacy, slow polling-based monitoring with a high-velocity, data-driven operating model.

At Amex I worked on a large-scale fraud analytics initiative, designed to evaluate billions of card transactions in near real time. The platform combined transaction behavior, contextual signals, and advanced analytics to improve fraud detection accuracy while minimizing customer disruption. This program strengthened risk controls, reduced financial losses, and enabled faster, more confident transaction decisions worldwide.

The engagement involved a global supply chain analytics program that unified manufacturing, logistics, and demand data across regions. The initiative enabled more accurate demand forecasting, better inventory alignment, and optimized transportation planning. Business teams gained timely, actionable insights to reduce operational costs and improve service levels across the end-to-end supply chain.

As a Big Data Engineer, I contributed to a centralized customer risk and credit assessment platform, used for automated credit decisioning and regulatory reporting. The solution consolidated customer, transaction, and external data to deliver consistent and explainable risk insights. This reduced manual underwriting effort, improved risk prediction accuracy, and strengthened compliance with regulatory requirements.

The project involved building an enterprise data warehouse to unify booking, customer, pricing, and partner data across multiple travel businesses. The platform enabled near real-time insights into demand, revenue performance, and customer behavior. It became the analytical backbone for business reporting, marketing optimization, and strategic decision-making.