Fish geolocation models estimate the probabilities of fish positions by comparing tag records from data storage tags (DST) with high-resolution reference fields of the same variables. Due to the complexity and size of such data, challenges arise for biologists. To tackle this issue, we are exploring the adaptability of the open-source Pangeo platform. Originally designed by the geoscience community for big data analyses, it aims to facilitate fish geolocation using biologging data and improve accessibility. We introduce "Pangeo-Fish", a novel geolocation tool designed for integrating fish tracking data. This innovative approach builds upon a Hidden Markov Model that infers pelagic fish positions and quantifies uncertainties from the sole use of high-resolution temperature and depth histories (Woillez et al., 2016). Based on practical use cases, we evaluate the performance of the geolocation model in terms of processing time and geolocation error using combined DST and acoustic telemetry data for different reference fields and grid resolutions. We illustrate Pangeo-Fish using biologging and acoustic telemetry data from European sea bass (Dicentrarchus labrax) and pollack (Pollachius pollachius) in the English Channel. Our results demonstrate that compared to the traditional approach, Pangeo-Fish reduces the analysis time substantially, showcasing its efficiency and potential to accelerate biologging data analysis in a standardized way. This allows biologists to easily select the optimal reference fields and grid resolution for their specific fish tracking application. As part of our ongoing efforts, we are currently implementing Pangeo-Fish into the Destination Earth (DestinE) core service platform as part of the Global Fish Tracking System project. This cloud-based solution leverages DestinE, an initiative of the European Commission aiming to develop a highly accurate global-scale digital model of the Earth, to advance marine species behavior analysis using ocean temperature data. Pangeo-Fish integration promises improved data analysis and insights for marine life conservation and research.