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 Environment (DAVE). The proposed Hybrid Model Engineering Engine (HMEE) integrates domain knowledge and machine learning components as configurable operators embedded in directed acyclic graph pipelines, enabling structured experimentation and lifecycle management within a unified platform. As a first representative hybrid strategy, residual learning is implemented and demonstrated in a real-world residential energy use case. A physics-informed thermal model provides baseline predictions, while a machine learning model learns to correct systematic deviations through residual modelling. The results illustrate how hybrid workflows can be engineered, executed, and evaluated in a structured and reproducible manner.