Fog computing can support IoT services with fast response time and low bandwidth usage by moving computation from the cloud to edge devices. However, existing fog computing frameworks have limited flexibility to support dynamic service composition with a data-oriented approach. Function-as-a-Service (FaaS) is a promising programming model for fog computing to enhance flexibility, but the current event- or topic-based design of function triggering and the separation of data management and function execution result in inefficiency for data-intensive IoT services. To achieve both flexibility and efficiency, we propose a data-centric programming model called Fog Function and also introduce its underlying orchestration mechanism that leverages three types of contexts: data context, system context, and usage context. Moreover, we showcase a concrete use case for smart parking where Fog Function allows service developers to easily model their service logic with reduced learning efforts compared to a static service topology. Our performance evaluation results show that the Fog Function can be scaled to hundreds of fog nodes. Fog Function can improve system efficiency by saving 95% of the internal data traffic over cloud function and it can reduce service latency by 30% over edge function.