  As quantum computing devices increase in size with respect to the number of
qubits, two-qubit interactions become more challenging, necessitating
innovative and scalable qubit routing solutions. In this work, we introduce
beSnake, a novel algorithm specifically designed to address the intricate qubit
routing challenges in scalable spin-qubit architectures. Unlike traditional
methods in superconducting architectures that solely rely on SWAP operations,
beSnake also incorporates the shuttle operation to optimize the execution time
and fidelity of quantum circuits and achieves fast computation times of the
routing task itself. Employing a simple breadth-first search approach, beSnake
effectively manages the restrictions created by diverse topologies and qubit
positions acting as obstacles, for up to 72\% qubit density. It also has the
option to adjust the level of optimization and to dynamically tackle
parallelized routing tasks, all the while maintaining noise awareness. Our
simulations demonstrate beSnake's advantage over an existing routing solution
on random circuits and real quantum algorithms with up to $1,000$ qubits,
showing an average improvement of up to $80\%$ in gate overhead and $54\%$ in
depth overhead, and up to $8.33$ times faster routing times.
