In the field of chemistry, fitting data is a standard part of the workflow. The data can come from either experiments or theoretical models , but being able to process and quantitatively analyze the data is crucial in order to draw reliable and thorough conclusions. This is especially true in spectroscopy, where fitting is necessary to match experimental peaks to a quantum chemical or semi-empirical model. However, the process of fitting can be complicated and time-consuming, and finding ways to streamline these processes could significantly enhance workflow efficiency. Here, we see two opposite approaches to fitting spectra. On the one hand, there are the socalled "black-box" fitting approaches, which allow users to fit spectra with a few clicks and are often implemented in commercial software like Origin or IgorPro. On the other hand, there are the "white-box" fitting approaches, where users usually have to program their own individual fitting routines. One established fitting routine for "whitebox" approaches is "lmfit" developed by M. Newville and contributors. As the detected spectra (such as resonant inelastic X-ray scattering (RIXS) , X-ray magnetic circular dichroism (XMCD) , and time-resolved spectroscopies ) become more complex, users often have to create their own "white-box" solutions and spend a significant amount of time developing and testing their fitting routines. Our previous challenges with fitting 2p3d RIXS spectra have led us to aim for the integration of both techniques. Our goal is to create a user-friendly tool for fitting spectroscopic data. Furthermore, our fitting tool offers an uncomplicated approach to customizable methodologies. The proposed software tool, named SpectraFit, has been released as an open-source project on GitHub (). We value input from the scientific, software development, and user communities as it promotes the key principles of interdisciplinary research, such as transparency, collaboration, accessibility, and customization. The SpectraFit program offers a comprehensive approach to data processing and fitting, utilizing both black-box and whitebox techniques. The black box approach utilizes pre-existing fitting and data processing routines that are consistently integrated into the program. In contrast, the white box approach enables users to bind to a Jupyter-Notebook or import the program as a package in a different application, providing extensive customization options for data preand post-processing (SI -Jupyter). With this approach, chemists are empowered to explore the interaction between data, model, and result, functioning as data engineers. The customized program is, in principle, able to handle all types of imported and exported data formats, with or without feature engineering. However, the fitting procedure of SpectraFit itself remains a core component that cannot be altered.