Senior Software Engineer

Overview

At Sigmoid Analytics, I worked on building high-performance data processing pipelines that handle millions of events per second. My primary focus was on optimizing the real-time analytics infrastructure.

Key Achievements

40% Latency Reduction

• Redesigned the data ingestion pipeline using Apache Kafka
• Implemented efficient batching strategies for downstream processing
• Optimized serialization/deserialization using Protocol Buffers

Real-time Data Processing

• Built streaming data pipelines using Spring Boot and Kafka Streams
• Implemented exactly-once semantics for critical data flows
• Designed fault-tolerant consumer groups with automatic rebalancing

System Optimization

• Profiled and optimized JVM garbage collection settings
• Implemented connection pooling for database operations
• Reduced memory footprint by 30% through efficient data structures

Technical Stack

• Languages: Java, Python
• Frameworks: Spring Boot, Kafka Streams
• Messaging: Apache Kafka
• Databases: PostgreSQL, Redis
• Infrastructure: AWS, Docker, Kubernetes

Challenges & Solutions

Challenge: High Latency in Event Processing

The existing system had P99 latency of 500ms which was unacceptable for real-time analytics.

Solution: Implemented a multi-stage pipeline with backpressure handling, optimized consumer configurations, and introduced caching for frequently accessed reference data.

Challenge: Data Consistency Across Services

Multiple services needed consistent views of the same data.

Solution: Implemented event sourcing pattern with Kafka as the source of truth, ensuring all services eventually converge to the same state.