We consider a longitudinal data structure consisting of baseline covariates, time-varying treatment variables, intermediate time-dependent covariates, and a possibly time dependent outcome. Previous studies have shown that estimating the variance of asymptotically linear estimators using empirical influence functions in this setting result in anti-conservative estimates with increasing magnitudes of positivity violations, leading to poor coverage and uncontrolled Type I errors. In this paper, we present two alternative approaches of estimating the variance of these estimators: (i) a robust approach which directly targets the variance of the influence function as a counterfactual mean outcome, and (ii) a non-parametric bootstrap based approach that is theoretically valid and lowers the computational cost, thereby increasing the feasibility in non-parametric settings using complex machine learning algorithms. The performance of these approaches are compared to that of the empirical influence function in simulations across different levels of positivity violations and treatment effect sizes.