If your data cleaning takes 80% of your time, your pipeline is broken. At my previous company, I dealt with 500GB of raw, multi-source data. Initially, it was a bottleneck. By shifting to optimised SQL stored procedures and Python-based pre-processing (Pandas/NumPy), we cut