I struggled through the same thing in engineering school.
Part of the issue is that it is easy to see how linear algebra works in 2- or 3-dimensional Euclidean space. It's the most basic algebra, solving simultaneous equations with straight lines. In fact, most of what you study in classic high school algebra is about getting away from such a simplistic and rigid constraint - i.e., linearity in $x$ and $y$. So, I agree, a lot of the formalism you get in engineering school feels almost like clever notation to explain the most basic algebra in n-dimensional space. Notably, matrix algebra feels like a very compact notational scheme for solving simultaneous equations.
There are a few things that will become more clear over time, however, as you study linear algebra and other applications of it, and they come from greater generalization. What you find is that "linear algebra" (with a little 'l', meaning Euclidean space) is really a degenerate case of a much broader set of principles and tools.
For example, eventually you learn in solving differential equations that a great many ones of practical interest have the form
$$y(t)=\sum_n \sin(n\omega t)$$
where $n$ may be infinity. Now, it will turn out that $\sin(n\omega t)$ for a set of $n$'s can be thought of as the (orthogonal) basis for a linear space, just as $\hat i,\hat j, \hat k$ form the basis for a linear Euclidean space with axes $x, y,$ and $z$. And a whole bunch of the machinery that applies to Euclidean space will work here as well.