The offshore renewable energy sector heavily relies on satellite Earth observation data to assess wind, waves, and ocean conditions for site planning and operations. However, modern satellites generate massive, multi-dimensional datasets. Traditional, local computing environments and monolithic file systems simply cannot handle this volume efficiently, creating severe bottlenecks in storage, processing, and data accessibility. To overcome these bottlenecks, this thesis designs and implements a scalable, cloud-native processing pipeline hosted on Google Cloud Platform (GCP). The architecture shifts the heavy lifting from local workstations to a distributed network, utilizing a producer- consumer model to execute tasks asynchronously. Key Architectural Implementations includes: 1.