Neuroimaging data serves as a rich source of information, particularly when it comes to understanding brain function and structure. This amount of detail is often accompanied by expensive computational demands due to the high dimensional nature of the data - encompassing various modalities such as structural and functional MRI data. The processing pipeline of such data involves a pre and a post-processing stage. To date, only the pre-processing which involves denoising of data among others, has been standardized in a community-driven manner. For the post-processing stage which involves extracting meaningful measures for statistical analyses or machine learning, researchers usually build or adopt ad hoc pipelines specific to their use-case. This not only makes the step less reproducible due to manual intervention but also makes it extrememly difficult for others to audit the code. To tackle this, we have developed junifer, a neuroimaging feature extractor, aimed at large-scale datasets. junifer automates key post-processing tasks with low computational overhead. The post-processing pipeline currently comprises of dealing with many intermediary tools like AFNI, ANTs, FSL and FreeSurfer, each with their individual file formats and brain atlases. junifer stands on the shoulder of these giants and provides a consistent interface to facilitate the process and automates the data transformation into correct brain atlas space. Thus, a non-technical researcher can focus on their analysis and not build ad hoc pipelines. junifer’s configuration files can be easily shared allowing researchers to replicate feature extractions in a standardized manner. The extracted features and metadata are stored in HDF5 or SQLite files, ensuring traceable data provenance. junifer supports both API-based integration and standalone application use, democratizing access to neuroimaging analysis.