We present a context-aware diffusion model for multivariate time series generation in dynamic and partially observed environments, with applications to data-center computing node's telemetry and beyond. The model integrates pretrained textual embeddings to represent feature semantics, enabling flexible, context-guided generation and improved adaptability to unseen or re-ordered input features. Built on a transformer architecture, it employs both time-wise and feature-wise masking to support missing data during training and inference. We show that the model is robust to permutations with respect to the feature dimension, mantaining stable performance in settings where input configurations vary. Empirical evaluations on HPC sensor data illustrate the model’s versatility across generation and imputation tasks. This work introduces a modular and generalizable framework for time series modeling in complex, high-dimensional systems which can serve as a digital-twin for data-center's compute node telemetry.