Reliable estimates of occurrence probabilities of sea level extremes are required in coastal planning (e.g. design floods) and to mitigate risks related to flooding. Probabilities of specific extreme events have been traditionally estimated from the observed extremes independently at each tide gauge location. However, this approach has shortcomings. Firstly, sea level observations often cover a relatively short historical time period and thus contain only a small number of extreme cases (e.g. annual maxima). This causes substantial uncertainties when estimating the distribution parameters. Secondly, exact information on sea level extremes between the tide gauge locations and incorporation of depen- dencies of adjacent stations is often lacking in the analysis. A partial remedy to these issues is to exploit spatial dependencies exhibited by the sea level extremes. These dependencies emerge from the fact that sea level variations are often driven by the same physical and dynamical factors at the neighboring stations. Bayesian hierarchical modeling offers a way to model these dependencies. The model structure allows to share information on sea level extremes between the neighboring stations and also provides a natural way to represent modeling uncertainties. In this study, we use Bayesian hierarchical modeling to estimate return levels of annual sea level maximum in the Finnish coastal region, located along the north-east part of the Baltic Sea. As annual maxima are studied, we use the generalized extreme value (GEV) distribution as the basis of our model. To tailor the model specifically for the target region, spatial dependencies are modeled using physical covariates which reflect the distinct geometry of the Baltic Sea. We illustrate the added value of the hierarchical model in comparison to the traditional one using the available long-term tide gauge time series in Finland. Careful analysis of the sources of uncertainties is necessary when extrapolating the return level estimates into the future. This work is a part of project PREDICT (Predicting extreme weather and sea level for nuclear power plant safety) that supports nuclear power plant safety in Finland.