You should ignore the fact that this article is about data scientists and addressed to python developers (complete with the memes they'll resonate to). Read from the perspective of a learning professional, this article is full of insights as to the sorts of skills that will be valuable in the future, not just for data engineers, but for everyone. For example: is discusses how to develop an AI harness, and links to an OpenAI blog post on harness engineering. That's the sort of thing we saw in this week's Claude Code software. Knowing about the 'observability stack' - logs, metrics, and traces - is key. "Explore the data, explore the traces, ask 'what is actually breaking here?', and figure out the highest-value thing to start measuring. There are infinite things to measure. You have to form hypotheses and iterate." The article lists a series of 'pitfalls' but this is just the beginning of the list of things worth learning.
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