Satellite and in-situ snow observations are increasingly available, but their assimilation into physically based snow modeling within a reproducible workflow from data preprocessing to model diagnostics remains a challenge. We present openAMUNDSEN-DA v0.9, an open source ensemble based snow data assimilation framework coupled to the fully distributed snow hydrological model openAMUNDSEN. The new framework enables sequential particle filter updates within a configured, traceable workflow while keeping the original snow model setup separate from data assimilation settings. Prior uncertainty is represented by perturbing the meteorological forcing, ensuring each ensemble member follows a physically consistent simulation trajectory. The workflow covers observation preprocessing, event scheduling, ensemble execution, likelihood based member weighting, effective sample size diagnostics, resampling, rejuvenation, posterior generation, benchmarking, and the production of plots, maps and concise reports. Supported observation types include station snow depth and snow water equivalent, and satellite based snow cover fraction, wet snow fraction, and wet snow line altitude. For each assimilation experiment, observation specific model equivalents and uncertainty settings are defined in the project configuration. We illustrate the capabilities of the new framework in a case study from the Rofental research catchment (Ötztal Alps, Austria), sequentially assimilating in situ snow depth, satellite snow cover and wet snow observations.