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The advent of computational and data-driven materials engineering has transformed materials discovery by integrating machine learning with high-throughput simulations and experimental workflows. Within this ecosystem, feature engineering emerges not merely as a technical preprocessing step but as a fundamental scientific framing mechanism that encodes domain knowledge into data representations, influencing inference pathways and discovery outcomes. This conceptual manuscript explores how encoding choices in materials informatics shape epistemic structures, steering computational pipelines from raw multimodal datasets to inverse design strategies. We identify a conceptual gap in current paradigms, where representation decisions often remain implicit, leading to unexamined trade-offs in uncertainty propagation and model interpretability. To address this, we introduce the Encoding Dynamics Framework (EDF), a systems-level architecture that conceptualizes feature engineering as an interactive layer between data infrastructures and AI-guided discovery systems. EDF highlights feedback loops where encoding selections modulate representation learning, graph neural networks, and closed-loop experimentation, fostering more robust computational steering logics. Implications extend to foundation models for materials science, simulation-experiment coupling, and uncertainty quantification, promoting infrastructures that align encoding with scientific inquiry goals. By reframing feature engineering as epistemic framing, this work advances interpretive insights into how data encoding choices drive materials innovation without empirical validation.