The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on beginners (students) of data engineering. In this article, we present a classification of these common mistakes, including recommendations not only for how data engineering tools might avoid some of them, but also for how these insights can inform teaching strategies and educational tool design. We believe that our work will help researchers, practitioners, and educators alike build better and more effective tools and learning environments in the future.