We study inference on dynamic average treatment effect parameters for staggered interventions when parallel trends are only valid conditional on unobserved interactive fixed effects. Our identification strategy allows for any first stage system of moments that controls the column space of the unobservable trends including principal components, common correlated effects, quasi-differencing, and more. This result applies to data sets with either many or few pretreatment time periods. We also demonstrate the robustness of two-way fixed effects to certain parallel trends violations and describe how to test for its consistency. We investigate the effect of Walmart openings on local economic conditions and demonstrate that our methods ameliorate pre-trend violations commonly found in the literature.