Cryospheric data is increasing in size, demanding highly computational analyses. Open science principles, including collaboration, enable efficient, tested, reproducible, and diverse computational resource development. The ICESat-2 science community continues to coalesce around these ideals through contributions to icepyx, a community and open-source Python library for obtaining and working with large (~500 GB/day) data products from the ICESat-2 satellite/ATLAS laser altimeter. Our presentation will focus on the history, motivation, and process of creating this community, developing shared computational tools, and collating a set of example workflows within Jupyter Notebooks focused on ICESat-2 data. We will present new and in-the-works examples and features of the library, including enhanced pre-data-download visualizations, collaborative developments for multi-mission and -sensor data access, and data read-in/merging functionality. We will also highlight the community building events (including hackweeks) that drive this group and showcase some of the research supported and enabled by this software library.