Worked on performance optimization within the qua-platform/qua-libs repository, focusing on improving the efficiency of averaging computations in data processing workflows. Refactored the Python codebase by restructuring the loop logic, moving assignment statements into a nested loop executed after the averaging pass. This targeted change reduced redundant operations and enhanced throughput for analytics pipelines, resulting in faster runtime performance. The approach emphasized maintainability by clearly separating the averaging and assignment phases, minimizing risk while improving code clarity. Utilized skills in code optimization and software development to deliver a focused feature update, addressing a specific bottleneck without introducing new bugs or regressions.