Hybrid models combine first-principles knowledge with machine learning to enhance predictive performance while preserving physical consistency and interpretability. Despite their advantages, such approaches are often developed in a problem specific manner and lack standardized workflows that support reuse and systematic experimentation. To address this challenge, this work proposes a modular, pipeline-based framework for hybrid model engineering within the Data Analytics and Visualization Enviro