Penalty methods are a well known class of algorithms for constrained optimization. They transform a constrained problem into a sequence of unconstrained penalized problems in the hope that approximate solutions of the latter converge to a solution of the former. If Lagrange multipliers exist, exact penalty methods ensure that the penalty parameter only need increase a finite number of times, but are typically scorned in smooth optimization for the penalized problems are not smooth. This led researchers to consider the implementation of exact penalty methods inconvenient. Recently, advances in proximal methods have led to increasingly efficient solvers for nonsmooth optimization. We show that the exact \(\ell_2\)-penalty method for equality-constrained optimization can in fact be implemented efficiently by solving the penalized problem with a proximal-type algorithm. We study the convergence of our algorithm and establish a worst-case complexity bound of \(\mathcal{O}(\epsilon^{-2})\) to bring a stationarity measure below \(\epsilon > 0\) under the Mangarasian-Fromowitz constraint qualification and Lipschitz continuity of the objective gradient and constraint Jacobian. In a degenerate scenario where the penalty parameter grows unbounded, the complexity becomes \(\mathcal{O}(\epsilon^{-8})\), which is worse than another bound found in the literature. We justify the difference by arguing that our feasibility measure is properly scaled. Finally, we report numerical experience on small-scale problems from a standard collection and compare our solver with an augmented-Lagrangian and an SQP method. Our preliminary implementation is on par with the augmented Lagrangian in terms of robustness and efficiency. It is on par with the SQP method in terms of robustness, though the former remains ahead in terms of number of problem function evaluations.