As society transitions from traditional engineered physical systems to software-driven ecosystems, the demand for adaptive, cost-effective, and rapidly evolving technologies continues to rise. Digital Twins (DTs) have recently emerged as a key architectural and conceptual tool to address these needs by decoupling high-level system control and decision-making from physical operations. By integrating deductive engineering models with inductive, data-driven insights, DTs offer powerful capabilities for simulation, prediction, and adaptive system management. However, despite their promise, current DT implementations often remain fragmented, domain-specific, and expensive to build and maintain.

This paper proposes a structured and principled approach to the engineering of digital twins (EDT), grounded in established software and systems engineering practices, to enable the development of scalable, reusable, and trustworthy DTs. Building on the capabilities outlined by the Digital Twin Consortium, we offer a complementary perspective by identifying key foundational research challenges associated with their integration in the context of digital twin engineering. In addition, we examine the complete engineering lifecycle of DT systems and present a comprehensive research agenda for the EDT community.