The growing adoption of data-driven techniques to analyze sensor data in production engineering reinforces the significance of time series in research and development. In this context, time series management is on the verge of being reconceptualized. Modern data mining pipelines depend on seamless data flows and reproducible datasets. However, data acquisition and analysis are often treated as separate processes, creating a disconnect between tools. To create a unified time series management system for acquisition and analysis, two challenges must be addressed: contrary storage requirements and rapidly evolving information models.This paper proposes a data management solution for time series and related metadata for mechanical engineers seeking to aggregate and analyze data from machine prototypes, test beds, or small pilot lines. The solution integrates the previously disparate activities of data acquisition and analysis. This includes incorporating new sensors, as well as creating, augmenting, and analyzing labeled datasets. The solution is flexible by design and can be used with information models from OPC UA companion specifications, Asset Administration Shells, or as a standalone. A detailed description of the setup, tools, and validation on a machine tool is provided.