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These approaches show good performance improvements for simple transformations, but struggle to handle different data characteristics (many features\/distinct items) and multi-pass transformations. A key observation is that good parallelization strategies for feature transformations depend on data characteristics. In this paper, we introduce UPLIFT, a framework for<jats:italic>P<\/jats:italic>aralle<jats:italic>LI<\/jats:italic>zing<jats:italic>F<\/jats:italic>eature<jats:italic>T<\/jats:italic>ransformations. UPLIFT constructs a fine-grained task graph for a set of transformations, optimizes the plan according to data characteristics, and executes this plan in a cache-conscious manner. We show that the resulting framework is applicable to a wide range of transformations. Furthermore, we propose the FTBench benchmark with transformations and datasets from various domains. 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