One way to tune step size in gradient descent is via backtracking line search.
backtracking line search (with parameters α ∈ (0, 1/2), β ∈ (0, 1))
starting at $t = 1$, repeat $t := \beta t$ until $f(x + t\Delta x) < f(x) + \alpha t\nabla f(x)t\Delta x$
There are suggestions about what range of values to use for $\alpha$ and $\beta$, however, none of these discuss the scale of either data or loss function. The question is shouldn't range of values of data and $f$ have conceptually some effect in selecting $\alpha$ and $\beta$? Is it assumed that data is normalized? How about objective value $f$?