Studying the thermodynamic properties of galaxy clusters is crucial for understanding the evolution of the universe at the largest scales. Galaxy clusters exhibit significant heterogeneity, even after rescaling, which makes large samples essential for accurately characterizing the variations across this population. I will present the results of a Bayesian Hierarchical Model estimation of the population-averaged pressure profile and intrinsic scatter for a sample of 55 galaxy clusters observed with SPT. This analysis simultaneously captures both the global properties of the entire cluster sample and the individual characteristics of each cluster through a joint estimation procedure. The analysis introduces a highly flexible, low-covariance parameterization of the pressure profile using restricted cubic splines, which provide a straightforward and intuitive interpretation of the parameters. The large sample size enables conducting morphologically specific analyses by dividing the clusters into three distinct morphological classes based on eROSITA data. I will discuss how the findings of this analysis compare with results from other studies in the literature.