How to Put Kafka on Your Resume

Built a real-time event processing pipeline in Go and Kafka that handles 2M+ events per hour with durable delivery guarantees, replacing a brittle cron-based system that regularly dropped messages during traffic spikes. The new pipeline has reduced missed events and manual reprocessing for over a year

Built a real-time data pipeline using Kafka and Spark Structured Streaming that ingests 5TB+ daily from 30+ source systems including transactional databases, API feeds, and clickstream events. The pipeline maintains sub-minute latency for downstream analytics consumers

Designed a real-time streaming pipeline using Kafka and Flink that processes 1M+ events per second for fraud detection with sub-second end-to-end latency from event ingestion to alert generation. The pipeline has maintained 99.99% uptime over 12 months of continuous operation

Architected a real-time streaming layer using Kafka and Spark Structured Streaming that processes 500K events per minute from IoT sensors, feeding both the operational dashboard and the batch analytics warehouse with a single ingestion pipeline.

Implemented a Kafka-based event streaming pipeline that captures user interactions in real-time and updates user profiles within 30 seconds, enabling session-aware recommendations that improved next-click prediction accuracy by 15%.