Architecture matters because while deep learning can conceivably fit a curve with a single, huge layer (in theory... Universal approximation theorem), the amount of compute and data needed to get there is prohibitive. Having a good architecture means the theoretical possibility of deep learning finding the right N dimensional curve becomes a practical reality.

Another thing about the architecture is we inherently bias it with the way we structure the data. For instance, take a dataset of (car) traffic patterns. If you only track the date as a feature, you miss that some events follow not just the day-of-year pattern but also holiday patterns. You could learn this with deep learning with enough data, but if we bake it into the dataset, you can build a model on it _much_ simpler and faster.

So, architecture matters. Data/feature representation matters.