Worked on backend performance optimization for the AffineFoundation/affine repository, focusing on enhancing the data pipeline’s efficiency and reliability. Implemented a sink buffering mechanism using Python to reduce R2 writes, which improved throughput and lowered resource consumption. Additionally, tuned the TAIL parameter from 20,000 to 10,000, further optimizing latency and resource usage in the sink path. The work emphasized stability and scalability, prioritizing targeted optimizations over bug fixes during this period. Leveraged backend development and performance optimization skills to deliver a more robust data handling process, resulting in a more efficient and reliable pipeline for ongoing and future workloads.