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Embedded real-time systems like those found in automotive, rail and aerospace, steadily require higher levels of guaranteed computing performance (and hence time predictability) motivated by the increasing number of functionalities provided by software. However, high-performance processor design is driven by the average-performance needs of mainstream market. To make things worse, changing those designs is hard since the embedded real-time market is comparatively a small market. A path to address this mismatch is designing low-complexity hardware features that favor time predictability and can be enabled/disabled not to affect average performance when performance guarantees are not required. In this line, we present the lessons learned designing and implementing LEOPARD, a four-core processor facilitating measurement-based timing analysis (widely used in most domains). LEOPARD has been designed adding low-overhead hardware mechanisms to a LEON3 processor baseline that allow capturing the impact of jittery resources (i.e. with variable latency) in the measurements performed at analysis time. In particular, at core level we handle the jitter of caches, TLBs and variable-latency floating point units; and at the chip level, we deal with contention so that time-composable timing guarantees can be obtained. The result of our applied study with a Space application shows how per-resource jitter is controlled facilitating the computation of high-quality WCET estimates.