Worked on enhancing the neptune-fetcher repository by reintroducing parallel run creation during project population, focusing on improving performance for large-scale data operations. Leveraged Python’s multiprocessing and concurrency capabilities, specifically ProcessPoolExecutor, to accelerate throughput and reduce time-to-value for users. The implementation included robust error propagation from worker processes to the main thread, ensuring that exceptions were properly handled and reliability was maintained throughout the population workflow. This feature aligned with ongoing scalability and productivity goals, demonstrating practical application of concurrency control and testing skills in production code to deliver measurable efficiency improvements without introducing new bugs.