Dataflow applications, such as machine learning algorithms, can run for days, making it desirable to have assurances that they will work correctly. Current tools are not good enough: too often the interactions between tasks are not type-safe, leading to undesirable runtime errors.This paper presents a new declarative Haskell embedded DSL (eDSL) for dataflow programming: CircuitFlow. Defined as a Symmetric Monoidal Preorder on data that models dependencies in the workflow, it has a strong mathematical basis, refocusing on how data flows through an application, resulting in a more expressive solution that not only catches errors statically, but also achieves competitive run-time performance. In our preliminary evaluation, CircuitFlow outperforms the industry-leading Luigi library of Spotify by scaling better with the number of inputs.