Data wrangling, the multi-faceted process by which the data required by an application is identified, extracted, cleaned and integrated, is often cumbersome and labor intensive. In this paper, we present an architecture that supports a complete data wrangling lifecycle, orchestrates components dynamically, builds on automation wherever possible, is informed by whatever data is available, refines automatically produced results in the light of feedback, takes into account the user’s priorities, and supports data scientists with diverse skill sets. The architecture is demonstrated in practice for wrangling property sales and open government data.