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The barriers to AI engineering are crumbling fast

(blog.helix.ml)
250 points lewq | 3 comments | 14 Nov 24 14:56 UTC | HN request time: 0.792s | source
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JSDevOps ◴[14 Nov 24 15:05 UTC] No.42136819[source]▶
Is anyone instantly suspicious when they introduce themselves these days an "AI Developer"
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pjmlp ◴[14 Nov 24 15:32 UTC] No.42137171[source]▶
Yes, just like "Data Engineer" for knowing how to use Tableau or doing OLAP queries.
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Ldragon27 ◴[14 Nov 24 16:35 UTC] No.42137904[source]▶
Are you saying that true Data Engineers typically do more than just use Tableau or run OLAP queries, or do you see the title 'Data Engineer' itself as a bit of a red flag these days? I’m pretty early in my career and was leaning toward Data Engineering, but hearing stuff like this makes me wonder if going for SWE might be smarter.
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1. hobs ◴[14 Nov 24 16:40 UTC] No.42137954[source]▶
The better paying role is a Backend SWE (usually). A really a good data engineer is just a Backend SWE who has specific experience.

A bad one is a SQL analyst whose been promoted too much.

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2. bubbleRefuge ◴[14 Nov 24 18:03 UTC] No.42139112[source]▶
as as data engieer/data architect , agreed. there are allot of 'tools' experts who are data engineers but can't code a lick.
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3. hobs ◴[14 Nov 24 19:01 UTC] No.42139859[source]▶
Yep, that's where I am at - the amount of times people have talked to me about the most recent podcast where they've heard how a new tool will solve all their problems and really its some samey mix of CDC and bad python+sql is... a lot.

I think there's not a ton of political palatability for realizing most of their projects are like one API and a sane use of some SQL away.