Piyush Mujavadiya

Led end-to-end architecture of a data platform supporting over 100 data analysts, software engineers, and data scientists. Designed and deployed an event ingestion platform handling 10B+ events daily, leveraging Spring Boot Reactive Framework to significantly improve data pipeline throughput and reduce latency. Accelerated ML model deployment from 2 days to 30 minutes by implementing automated MLO

Helped create and manage a Central Data Lake with multiple integrations for different business verticals. Led the development of a real-time pipeline using Confluent Kafka, Spark Structured Streaming, and Confluent Kafka Connectors for customer transaction data at a large scale (60M+/day). Optimized Spark jobs, reducing resource utilization by 50% while maintaining performance.

Collaborated with multiple teams and management to understand requirements and design products for advanced telecom analytics. Optimized and re-architected Spark Jobs to cut resources by 80%. Built streaming services for real-time processing of 10M users using Java and Kafka.

Developed Spark Jobs to analyze multiple source data for Network Operators at Terabyte Scale. Migrated Legacy Spark Jobs from RDD to Datasets. Designed data models in HBase to support web applications and analytics.

Studied Computer Science Engineering at IPS Academy, Indore. The curriculum covered various aspects of computer science and engineering principles.