The overarching topic of this research project is academic misconduct in online assessments, aiming to understand students´ behavior and methods. To gain deeper insights, an eye-tracking experiment was conducted to capture when and how students engage in academic misconduct. Data from this experiment will reveal irregularities in cheating behavior. This paper presents a data engineering pipeline for the preparation of the future eye-tracking data analysis implemented in Python and a reasoning for the chosen order. Steps like Feature Selection, Data Preparation, Outlier Detection and Treatment, Filtering, Smoothing, and Normalization are included in this pipeline. We describe the data set, the setting and conduction of the experiment, and the data engineering pipeline. This article contributes to the current discussion of the preprocessing and analyse of eye tracking data.