Data engineering is essential in machine learning (ML) because it prepares data for model training and evaluation. Data engineers build the infrastructure that allows large volumes of structured, high-quality data to be stored, accessed, and processed efficiently. By creating data pipelines and handling ETL processes, they ensure that ML algorithms receive clean, consistent data. This foundation is crucial for accurate model performance and reliable results. Data engineering supports feature engineering, a critical step where relevant features are selected, optimized, and prepared for model training, which directly impacts the success of ML projects.