This study examined the characteristics of technology-enhanced statistical tasks written by 75 preservice mathematics teachers who used the ESTEEM project’s curriculum materials. In particular, it investigated the extent to which the tasks incorporated three key aspects related to best practices for teaching statistics: (a) analysis of large, multivariate, real datasets; (b) continual connection to context; and (c) engagement in the statistical investigation cycle. Regarding Key Aspect 1, the results showed that the tasks involved analysis of large (usually between 30 and 200 cases) datasets, with an average of 15 attributes provided per case. The vast majority of the datasets were also real, either from outside sources or collected by the students when the task would be implemented. Concerning Key Aspect 2, most tasks called for students to connect their work to the context from which the data was generated numerous times including during their orientation to the task, their reading of graphs made to display the data, and their interpretation of their analysis. Engagement in multiple phases of the statistical investigation cycle (Key Aspect 3) was asked for in the tasks as well. Hence, the ESTEEM project’s curriculum materials hold promise for supporting new teachers to plan meaningful technology-enhanced statistical tasks.