Dataflow applications, such as machine learning algorithms,can run for days, making it desirable to have assurances that they willwork correctly. Current tools are not good enough: too often the interactions between tasks are not type-safe, leading to undesirable runtimeerrors. This paper presents a new declarative Haskell Embedded DSL(eDSL) for dataflow programming: CircuitFlow. Defined as a Symmetric Monoidal Preorder (SMP) on data that models dependencies in theworkflow, it has a strong mathematical basis, refocusing on how dataflows through an application, resulting in a more expressive solutionthat not only catches errors statically, but also achieves competitive runtime performance. In our preliminary evaluation, CircuitFlow outperformsthe industry-leading Luigi library of Spotify by scaling better with thenumber of inputs. The innovative creation of CircuitFlow is also of note,exemplifying how to create a modular eDSL whose semantics necessitates effects, and where storing complex type information for programcorrectness is paramount.