This paper serves as a progress update to the proposal submitted at the CAIN 2024 Doctoral Symposium, which introduced the concept of an Experimentation Engine for optimizing data analytics workflows through user-driven experimentation. Over the past year, significant progress has been made in refining the framework to address diverse user needs by tailoring the experimentation process to different user types, including domain experts and data scientists. The updated Experimentation Engine emphasizes keeping the user in the loop throughout the entire experimentation process, enabling seamless interaction and iterative refinement. A demonstration video of the initial prototype of the Experimentation Engine showcasing its capabilities is available at \href{https://doi.org/10.5281/zenodo.14288226}. These advancements aim to streamline the experimentation process further, delivering accurate, meaningful, and trustworthy results while dynamically adapting to varying user requirements and system constraints.