The unparalleled volume of data generated has heightened the need for approaches that can consume these data in a scalable and automated fashion. Although modern data-driven, deep-learning-based systems are cost-efficient and can learn complex patterns, they are black boxes in nature, and the underlying input data highly dictate their world model. Knowledge graphs (KGs), as one such technology, have surfaced as a compelling approach for using structured knowledge representation to support the integration of knowledge from diverse sources and formats. We present Empower (EMPWR), a comprehensive KG development and lifecycle support platform that uses a broad variety of techniques from symbolic and modern data-driven systems. We discuss the sets of system design guiding principles used to develop EMPWR, its system architectures, and workflow components. We illustrate some of EMPWR’s abilities by describing a process of creating and maintaining a KG for the pharmaceuticals domain.