Developed targeted observability enhancements for the linkedin/venice repository by introducing Partition State Monitoring Metrics to track partition states and transitions within the system. This work involved creating a new Java class for state transition statistics and integrating it with existing state models, enabling the collection and consumption of detailed metrics. By updating Venice’s state models to utilize these new metrics, the developer improved backend monitoring and provided deeper insight into workload distribution and resource state changes. These enhancements support more data-driven capacity planning and facilitate faster incident analysis, leveraging skills in Java, backend development, and metrics tracking to address operational visibility.