Data cleaning is a time-consuming and error-prone manual process even with modern workflow tools like OpenRefine. Here, we present AutoDCWorkflow, an LLM-based pipeline for automatically generating data-cleaning workflows. The pipeline takes a raw table coupled with a data analysis purpose, and generates a sequence of OpenRefine operations designed to produce a minimal, clean table sufficient to address the purpose. Six operations address common data quality issues including format inconsistencies, type errors, and duplicates.To evaluate AutoDCWorkflow, we create a benchmark with metrics assessing answers, data, and workflow quality for 142 purposes using 96 tables across six topics. The evaluation covers three key dimensions: (1) **Purpose Answer**: can the cleaned table produce a correct answer? (2) **Column (Value)**: how closely does it match the ground truth table? (3) **Workflow (Operations)**: to what extent does the generated workflow resemble the human-curated ground truth? Experiments show that Llama 3.1, Mistral, and Gemma 2 significantly enhance data quality, outperforming the baseline across all metrics. Gemma 2-27B consistently generates high-quality tables and answers, while Gemma 2-9B excels in producing workflows that resemble human annotations.