Does time increase linearly relative to data size in a machine learning workflow? Let's say I am working with a dataset first containing 100 rows of data. I want to follow the normal process of doing some feature engineering, then training a model on this data. Running the whole script in bulk will take X minutes.
Now let's say I run the same script on the same data, this time containing 1000 rows though. Let's say this takes Y time. Would it be safe to assume that Y=10X? Or would the time grow in some other way relative to the size of the data?

NO
What you want to know about is more-or-less time complexity of the algorithm, an area of study in computer science. The answer is that it might not take ten times as long. That would be nice, but time might increase quadratically, so instead of $y = 10x$, you get $y = (10x)^2$, so $100$ times as long. Or it could be in cubic time, so $1000$ times as long. Or it could be in quartic time...
A famous example of a quadratic time complexity being reduced is the Fourier transform. In its raw implementation, it runs in quadratic time, $O(n^2)$. Through some clever manipulations, it can be reduced to $O(n\log(n))$, which is much faster.
An example where the data processing would be linear, as you originally thought it would, might be a unit conversion, perhaps taking measurements in miles and concerting them to kilometers.
