The bacterial extracellular matrix forms autonomously, giving rise to complex material properties and multicellular behaviors. Synthetic matrix analogues can replicate these functions but require exogenously added material or have limited programmability. Here, we design a two-strain bacterial system that self-synthesizes and structures a synthetic extracellular matrix of proteins. We engineered Caulobacter crescentus to secrete an extracellular matrix protein composed of an elastin-like polypeptide (ELP) hydrogel fused to supercharged SpyCatcher [SC(−)]. This biopolymer was secreted at levels of 60 mg/liter, an unprecedented level of biomaterial secretion by a native type I secretion apparatus. The ELP domain was swapped with either a cross-linkable variant of ELP or a resilin-like polypeptide, demonstrating this system is flexible. The SC(−)-ELP matrix protein bound specifically and covalently to the cell surface of a C. crescentus strain that displays a high-density array of SpyTag (ST) peptides via its engineered surface layer. Our work develops protein design guidelines for type I secretion in C. crescentus and demonstrates the autonomous secretion and assembly of programmable extracellular protein matrices, offering a path forward toward the formation of cohesive engineered living materials. IMPORTANCE Engineered living materials (ELM) aim to mimic characteristics of natural occurring systems, bringing the benefits of self-healing, synthesis, autonomous assembly, and responsiveness to traditional materials. Previous research has shown the potential of replicating the bacterial extracellular matrix (ECM) to mimic biofilms. However, these efforts require energy-intensive processing or have limited tunability. We propose a bacterially synthesized system that manipulates the protein content of the ECM, allowing for programmable interactions and autonomous material formation. To achieve this, we engineered a two-strain system to secrete a synthetic extracellular protein matrix (sEPM). This work is a step toward understanding the necessary parameters to engineering living cells to autonomously construct ELMs.