This paper investigates the potential of reducing greenhouse gas emissions in data centers by intelligently scheduling batch processing jobs. A carbon-aware scheduler, S.C.A.L.E (Scheduler for Carbon-Aware Load Execution), was developed and applied to a resource-intensive data processing pipeline at ING. The scheduler optimizes the use of green energy hours, times with higher renewable energy availability, and lower carbon emissions. The S.C.A.L.E comprises three modules for predicting task running times, forecasting renewable energy generation and electricity grid demand, and interacting with the processing pipeline. Our evaluation shows an expected reduction in greenhouse gas emissions of around 20% when using the carbon-aware scheduler. The scheduler’s effectiveness varies depending on the season and the expected arrival time of the batched input data. Despite its limitations, the scheduler demonstrates the feasibility and benefits of implementing a carbon-aware scheduler in resource-intensive processing pipeline.