
Worked on the Meesho/BharatMLStack repository to deliver a Kafka-based data ingestion feature focused on reliability and scalability. Developed a FeatureDataEvent consumer in Go, leveraging Protobuf for message deserialization and implementing batch processing, robust error handling, and graceful shutdown to ensure consistent data persistence. Enhanced observability by introducing ingestion metrics, supporting operational reliability and future scalability. Additionally, performed repository cleanup by removing outdated assets and IDE configuration files, reducing clutter and preventing developer conflicts. Demonstrated skills in backend development, event-driven architecture, and microservices, with attention to code hygiene and maintainability, laying a foundation for faster iteration and improved developer experience.
In May 2025, delivered a Kafka-based data ingestion feature and completed repository cleanup, focusing on reliability, performance, and developer hygiene. Key work includes a new FeatureDataEvent Kafka consumer with batch processing, Protobuf deserialization, data persistence, error handling, and metrics; plus removal of outdated assets and IDE configurations to reduce clutter and conflicts. These changes improve data reliability, observability, and developer experience, laying groundwork for scalable feature pipelines and faster iteration.
In May 2025, delivered a Kafka-based data ingestion feature and completed repository cleanup, focusing on reliability, performance, and developer hygiene. Key work includes a new FeatureDataEvent Kafka consumer with batch processing, Protobuf deserialization, data persistence, error handling, and metrics; plus removal of outdated assets and IDE configurations to reduce clutter and conflicts. These changes improve data reliability, observability, and developer experience, laying groundwork for scalable feature pipelines and faster iteration.

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