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This article is part of the AI for data engineers playlist. Click here to explore the full series.
Lately, everyone’s been asking me about AI. Not just curious questions, but nervous ones.
“What happens to data engineers when AI does all the building?”
“Should I be learning prompt engineering instead of SQL?”
“Is this the end of the pipeline game?
Some of those came from subscribers. Some from coworkers. Even my doctor!
So this month’s group coaching call, I made it all about AI.
Two smart questions came in from community members:
Will AI automate us out of our own jobs?
And if I had to build a data department from scratch today, where does AI even fit?
They couldn’t make the call live. But I answered anyway. Because I know they’re not the only ones thinking about this.
The 2 questions we tackled
I didn’t want this call to turn into another AI circlejerk with people quoting LinkedIn influencers and pretending like anyone actually knows what’s coming.
So I picked two real questions from real engineers doing real work.
They cut through the noise.
Question 1 from
:If we’re the ones building AI capabilities either to speed up work or scale to our parters, how do we make sure we don’t just build ourselves out of the job?
Question 2 from
:What are the most important points you would focus on when building a data department from scratch, keeping in mind new approaches and simplifications in processes provided by AI, and what pitfalls should be avoided?
These are the kind of questions you ask when you’re in the trenches and you actually care about doing the work right.
So I answered them the same way.
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