Extracting meaningful information from large amounts of astronomical data requires not only a clear understanding of how it was collected and the physics involved, but also a disciplined approach to the analysis. As an example, in my current research I work with EPRV (Extreme Precision Radial Velocity) data, which - like other areas of astronomy - requires disentangling instrumental and physical effects. Unfortunately, this process is often hindered by the absence of best practices which makes subsequent analyses and models inherit these complications, compromising their accuracy. As a part of my research, I apply my background in software engineering by building a standard framework for processing data that can be reused across research projects. In this talk, I will discuss recommendations for bridging the gap between software engineering and astronomical data analysis, how I’m applying these practices to my research, and methods for integrating these enhancements in other works.