Text-to-SQL parsing increasingly relies on high-quality Chain-of-Thought (CoT) data to train specialized models. However, eliciting accurate reasoning paths for complex queries remains a formidable challenge, causing existing methods to suffer from severe data underutilization and fragile free-form generation. To systematically resolve these issues, we propose MSI-SQL, a Multi-task Self-Iterative framework that enhances both data quantity and quality. To expand data scale, we introduce a verification-based self-iterative loop that progressively conquers difficult queries. To ensure reasoning stability, we design a masked SQL completion auxiliary task that serves as a structural anchor, explicitly constraining the CoT generation space. Coupled with a novel mask-augmented soft self-consistency mechanism during inference, evaluations on the BIRD and Spider benchmarks demonstrate that MSI-SQL establishes a new state-of-the-art for open-source Text-to-SQL parsers.