Given the options to optimize SQL, move read operations to replicas, shard data or go towards micro services, optimizing SQL is the easy choice.
However, sometimes everyone needs to chill and sharpen the tool they have at hand. It might prove much more capable than first anticipated. Or you may be holding the tool wrong to a degree.
SQL is definitely part of that journey, and more skills should be mastered if you’re going to do things like use Kafka, airflow or spark.
But that’s not how markets work.
Also a job title doesn’t really say much. On all of these 4 roles there’s people doing trivial work, as there’s people doing very deep technical work. Same with pushing the envelope on tech.
My argument is not an "ought" argument, it's an "is". I agree with you that data engineering "ought" to be, if anything, a more difficult specialized practice of software engineering, because you need all the software engineering skills and then also specialized data skills. But I'm saying that what it often "is", is more like a specialization of BI and analyst roles.
I think the OP is a pretty good demonstration of this. If "all you need is SQL", does that sound more like a specialization of a software engineer skillset or an analyst skillset? I think the latter... And sure you can say, "well the article is wrong, if all you need is SQL, that's not data engineering", but we're just back to "no true scotsman"; I believe it is common to see the role this way.
Is that really worth being livid over? Outlook is fine. I left gmail a few years ago and haven't looked back. I survived.