In 2018, the Gaia Collaboration of the European Space Agency released a massive public data set ("Gaia DR2") of over 1 billion stars in the Milky Way Galaxy, including stars that reside in old star clusters --- Globular Clusters. Inferring the distribution of stellar mass in these globular clusters is vital to testing physical theories about cluster evolution. While Gaia DR2 is a stellar data set (pun intended), it also presents statistical challenges such as measurement uncertainty, incompleteness, and truncation. By pooling information across multiple globular clusters in the Milky Way Galaxy through a hierarchical Bayesian model, we hope to better constrain the masses of these systems. At the same time, we need to develop a computationally efficient and reliable data analysis pipeline to work with all clusters simultaneously in the hierarchical Bayesian framework, because each globular cluster has on the order of 10,000-100,000 stars. In this talk, I will go over our most recent advancements in this project, including tests with simulated data.