Numerical methods have advanced to the point that many groups can perform detailed numerical simulations of atomizing liquid jets and replicate experimental measurements. However, the simulation results have not lead to a substantial advancement to our understanding of these flows due to the massive amount of data produced. In this work, a tool is developed to extract the physics that destabilize the jet's liquid core by leveraging dynamic mode decomposition (DMD). DMD is a data-driven reduced-order modeling technique that takes ideas from the Arnoldi method as well as the Koopman method to evaluate a nonlinear system with a low-rank linear operator. The method reduces the order of the simulation results from the original spatially and temporally varying data to a few key pieces of information. Most important of these are the dynamic modes, their time dynamics, and the DMD spectra. In this work, DMD is applied to the jet's liquid core outer radius, which is computed at azimuthal and streamwise locations, i.e., R(theta, x). With the DMD data, we obtain the dominant spatial modes of the system and their temporal characteristics. The dominant modes provide a useful way to collapse the large data set produced by the simulation into a length and time scale that can be used to initiate reduced-order models and numerically categorize the instabilities on the jet's liquid core.