Worked on the elastic/rally-tracks repository to enhance benchmarking capabilities and improve the reliability of performance measurements. Focused on Python development, benchmarking, and performance tuning, the work included implementing new metrics and updating existing ones for the limit-10 scenario, enabling more accurate and robust performance data collection. Additionally, increased warmup iterations were introduced to stabilize benchmark results and reduce measurement noise, resulting in more trustworthy data for stakeholders. All changes were carefully documented and traceable, supporting reproducibility and informed release decisions. The technical approach emphasized measurable improvements in benchmarking pipelines and performance testing, with a focus on reliability and maintainability.