shubhmishra

Designed a multimodal tracking platform that unified shipment visibility for 100K+ shipments daily, using Java and Python microservices, Kafka for asynchronous messaging, Elasticsearch for scalable data processing. Led the design and implementation of a global Ocean tracking system, reducing shipment status update delays by 40% and enabling real-time freight visibility for 80% of ocean shipments. Engineered a routing solution that automated complex ocean shipment workflows, directly contributing to over $20M in enterprise revenue. Architected a distributed system for high-throughput event processing capable of handling over 5 million events per minute with advanced monitoring, retry mechanisms, and fault-tolerant architecture. Facilitated design reviews and resolved blockers reducing feature delivery time by 20% across 3 teams. Mentored over 10+ junior engineers and owned the onboarding process for incoming engineers, promoting best practices in code quality, system design, and architecture.

Improved inventory accuracy by over 10% across 4,000 stores by creating backend microservices using Java that enabled real-time data synchronization for store managers. Delivered a full-stack order management system using Node.js and React.js, reducing order processing time. Built scanner application used by over 50,000 Tesco employees, reducing item picking errors by 25% through improved UI/UX and backend validations for operational efficiency.

Expanded third party content offerings by 30% by building scalable microservices using Java, Azure Cosmos DB and Blob storage to integrate over 10 Go-To-Market partners. Designed and deployed scalable backend systems for video uploads, short-form content, and web series, supporting secure authentication and authorization for over 100,000 users. Constructed a real-time analytics pipeline with Azure Data Factory, processing over 50,000 records every 3 hours to deliver automated insights to stakeholders. Executed performance testing with JMeter and Grafana, simulating high-load scenarios and reducing system latencies by 30% through optimization efforts.