The generalized t-distribution (GT) is well-known because of its flexibility in transforming into many popular distributions. However, implementation of data reconciliation (DR) estimator using GT noise is somehow difficult due to its complex structure. This work proposes two iterative algorithms to ease the complexity of the GT DR estimator, hence making it easy to implement even in a large-scale problem. We also point out the convergence condition for each algorithm. Some simulation examples are shown to verify the effectiveness of the proposed algorithms on computational time. The results from this work can also be applied to other data reconciliation estimators.