The rank aggregation problem seeks to combine multiple rank orderings of the same set of candidates into a single consensus ordering. Such problems arise in diverse domains, including web search, employment, college admissions, and voting. In this work we focus on the 1-median objective: given a set of m rankings over [n], the goal is to compute a ranking that minimizes the sum of its distances to all input rankings. We study rank aggregation under several classical distance metrics: Ulam distance, Spearman's footrule, Hamming distance, and Kendall-tau, as well as their weighted variants. Our contributions begin with a novel unified framework that identifies a key structural property: it suffices to focus on a small subset of rankings, where the corresponding local one-median provides a good approximation to the global median. This principle extends across these distance measures, yielding a general algorithmic framework for weighted rank aggregation. Building on this, we present a new approximation algorithm for rank aggregation under the Ulam distance that scales in the Massively Parallel Computation (MPC) model. Our algorithm computes a $(2-α)$-approximation, for a constant $α>0$, to the 1-median in a constant number of rounds, using local memory sublinear in n and total memory near-linear in n. We further design new MPC approximation algorithms for Spearman's footrule and for the element-weighted variants of Hamming and Kendall-tau distances. For each metric, we obtain a $(2-ζ)$-approximation, for a constant $ζ>0$, to the 1-median in a constant number of rounds, using local memory sublinear in n and total memory linear or near-linear in n. Moreover, for the Ulam distance, we simplify and strengthen the analysis of Chakraborty et al., obtaining an improved 1.968-approximation that further extends to the weighted setting.