Both research and practice are increasingly using data to drive innovation. While innovative technology has the potential to better our day to day lives, we too often see examples of innovative technology doing more harm than good. These elevate concerns around ethics and equity, and point to the need for interventions that support intersectional analyses that prevent harm to marginalized communities. Traditional development tools narrowly focus on solving technical challenges, without helping practitioners make explicit connections to the potential impact their contributions may have, which studies have shown is an important factor when changing behavior in software teams. In this showpiece, we introduce Q UINTESSENCE , a novel proof-of-concept tool that supports reflexivity, as it pertains to intersectional identities, in data analysis and modeling. In the future, we plan to work with practitioners to improve Q UINTESSENCE for practical use, including a deeper dive into effective reflexive support and the addition of more analytic support in the modeling phases.