The Ariel Data Challenge 2024 tackles one of astronomy's hardest data analysis problems - extracting faint exoplanetary signals from noisy space telescope observations like the upcoming Ariel Mission. A major obstacle are systematic noise sources, such as ``jitter noise" arising from spacecraft vibrations, which corrupts spectroscopic data used to study exoplanet atmospheres. This complex spatio-temporal noise challenges conventional parametric denoising techniques. In this challenge, the jitter time series is simulated based on Ariel's payload design and other noise effects are taken from in-flight data from JWST, in order to provide a realistic representation of the effect.To recover minute signals from the planet's atmosphere, participants must push boundaries of current approaches to denoise this multimodality data across image, time, and spectral domains. This requires novel solutions for non-Gaussian noise, data drifts, uncertainty quantification, and limited ground truth. Success will directly improve the Ariel pipeline design and enable new frontiers in characterising exoplanet atmospheres - a key science priority in the coming decades for understanding planetary formation, evolution, and habitability.