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The serverless paradigm brings cloud computing close to not specialized programmers, with principles such as simplification, scalability, and pay-per-use billing. Serverless architectures open the way to process otherwise unimaginable data volumes from a standard personal computer, removing embarrassing resource provisioning tasks. In this context, different frameworks for serverless data analytics have emerged during the past years, like PyWren and Lithops, among others. However, the stateless nature of serverless functions makes it difficult for them to host workloads with heavy communication between functions. Shuffle-like jobs, like the MapReduce sort, are especially problematic in such conditions. Current solutions for serverless shuffle jobs are not fully transparent to the user and do not fulfill the principles of the serverless paradigm. In this work we introduce a completely transparent serverless sort utility built on top of Lithops that takes the user away from any resource management. We include uncommon concepts in serverless systems such as speculative and asynchronous MapReduce execution. We present a mathematic model to infer the optimal number of workers for each sort job and we prove its effectiveness. Finally, we test the performance of our algorithm against a standardized sort benchmark.