Progress in the last decade shows that the success of an ML system depends largely on the data it was trained on. Instead of focusing on improving ML algorithms, most companies focus on managing and improving their data.
Garbage in and garbage out. If an ML engineer is not obsessed about data quality (e.g. feature/label correctness, dataset distributional properties, etc), it's a clear indication that they don't understand how ML actually works.
