In this work, we present the process development strategy for a chemo-enzymatic synthesis of a macrocyclic peptide. The process required good control of reagent charges and faced sensitivities to process mixing and variability in operational performance; this was notably demonstrated in the workup and isolation processes due to sensitivities inherent in the process landscape. By leveraging appropriate engineering scale-up parameters, data-rich experimentation strategies, and common process modeling tools (i.e. Dynochem) we were able to both improve our process understanding and inform process targets at scale. This expansive dataset across scales proved useful for benchmarking and troubleshooting the process and enabling important scale-down investigations to the lab to take place. We also show the changes made in the end-to-end process design through development that showed significant improvements in volumetric productivity and improved process simplicity and robustness whilst also demonstrating that change inevitably involves additional technical challenges. This development strategy addressed numerous technical challenges across multiple unit operations as the process increased in scale; ultimately, the methodology employed showed successful process demonstrations at scale in good agreement with lab results enabling the team to successfully produce the intermediate at the desired process targets.