We have a sister company with many data scientists, and very few (actually I don't think that they ever hired any with the specific title) data engineers.

And, their production alleged "machine learning" (it's pretty much standard linear regression, but calling it ML is sexy) systems are slow motion train-wrecks. If the string and duct tape holds, then it works, but it's unfortunately continually breaking.

Hell, in Slack, I watch their data scientists continuously wrestle with how to actually make their Jupyter notebooks work in production.

Whereas my company has far more data engineers than data scientists. The plan from higher up the corporate food chain was always that we'd give them our data to do their data science voodoo on, but we ended up getting a few data scientists of our own for specific projects.

So, we focused on ensuring our data stream was reliable, consistent and sufficiently timely, for them to work on. But as soon as it hits their systems, it's a forest fire of hacks upon hacks, which inevitably break.

In the end, we had to send in our data engineers to stabilise their flagship "real time reporting" product that corporate was so amped about.

So yeah, I think that there's probably a happy ratio of data scientists to data engineers, of about, say, 1:5 or 1:10, because the maths generally scales O(1), it's the beauty of maths, but the actual engineering to get clean data delivered timely without breaking anything scales very differently indeed.