Robust design, informed decision-making, and effective diagnostics in Computational Science and Engineering (CSE) necessitate the integration of Uncertainty Quantification (UQ). This process requires developing computational models that provide reliable predictions while accounting for uncertainty, typically resulting in complex computational workflows. A key obstacle is bridging the gap between modeling experts, who have limited experience with UQ techniques, and UQ experts, who often apply their techniques to overly simplified models. Another critical challenge is building robust workflows in the presence of inherent heterogeneity among computational models, data formats, analysis techniques, compute environments, and human expertise. Furthermore, experimentation is essential for verifying and validating UQ tasks; it involves determining suitable priors and likelihood functions in Bayesian contexts, assessing Markov-Chain Monte-Carlo convergence and result quality, all while ensuring consistent outcomes despite stochastic influences. We demonstrate how to address these challenges through an open-source workflow for Bayesian parameter calibration in blood hemolysis models that we developed. The workflow leverages principles of isolation, interoperation, and orchestration, which form the foundation of the SHOWME.how approach [1]. We use cross-platform package managers and containerization technologies to isolate computational units that can interoperate and exchange data through language-agnostic data formats and platform-agnostic transfer protocols. It allows us to bridge the gap between modeling experts and UQ practitioners while effectively managing workflow heterogeneity. Finally, we orchestrate these computational units and data using modern graph-based workflow managers to build modular, scalable workflows that facilitate efficient execution of multiple UQ experiments. The demonstrated workflow can be used to test various research hypotheses while being easily adaptable for different computational models or UQ tasks. This enables rapid integration of state-of-the-art UQ techniques into practical applications across diverse domains within CSE.