Semi-structured data has become increasingly popular in data science and agile software development due to its ability to handle a wide variety of data formats, which is particularly important in data lakes where raw data is often semi-structured or ambiguous. Model-driven engineering (MDE) tools can provide a high-level, abstract representation of a system or process, making it easier to understand and navigate data. However, relying on data models to describe metadata of raw data can create challenges when working with semi-structured data, which can contain errors, inconsistencies, and missing data. In this work, we present a pragmatic approach to data-centric application development using MDE that complements current MDE practices. Our approach uses flexible models to enable agility and adaptability in working with data, compared to traditional metamodel-based methods. We propose a new metamodel for characterizing such models, with the aim of enabling the development of data-centric applications that do not require an explicit schema or metamodel. Our work demonstrates the feasibility of working with flexible models in a wide range of model to model transformation languages, particularly when handling semi-structured data sources.