A new approach is developed for efficient data assimilation into adaptive mesh simulations with the ensemble Kalman filter (EnKF). The EnKF is combined with a wavelet-based multi-resolution analysis (MRA) scheme, namely to enable robust and efficient assimilation in the context of reducedcomplexity, adaptive spatial discretization. The wavelet representation of the solution enables us to use a different meshes that are individually adapted to the corresponding member of the EnKF ensemble. The analysis step of the EnKF is then performed by involving coarsening, refinement, and projection operations on the members. Depending on the choice of these operations, five variants of the MRA-EnKF are introduced, and tested on the one dimensional Burgers equation with periodic boundary condition. The numerical results suggest that, given an appropriate tolerance value for the coarsening operation, four out of the five proposed schemes significantly reduce the computational complexity of the data assimilation, with marginal accuracy loss with respect to the reference EnKF solution. Overall, the proposed framework offers the possibility of capitalizing on the advantages adaptive mesh techniques, and the flexibility of choosing suitable context-oriented criteria for efficient data assimilation.