This paper proposes a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. The goal is to maximize the system sum rate by jointly optimizing the digital beamformer, analog beamformer, and positions of sub-arrays, under constraints such as unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function and highly coupled variables, the problem is challenging to solve. The authors employ fractional programming to develop an alternating optimization framework that combines Lagrange multipliers, penalty methods, and gradient descent. Numerical results show that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to fixed-position antenna (FPA) systems, and with sufficiently large movable regions, it even outperforms fully-connected FPA arrays.This paper proposes a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. The goal is to maximize the system sum rate by jointly optimizing the digital beamformer, analog beamformer, and positions of sub-arrays, under constraints such as unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function and highly coupled variables, the problem is challenging to solve. The authors employ fractional programming to develop an alternating optimization framework that combines Lagrange multipliers, penalty methods, and gradient descent. Numerical results show that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to fixed-position antenna (FPA) systems, and with sufficiently large movable regions, it even outperforms fully-connected FPA arrays.