Developed a foundational persistence layer for limit distribution data in the PySATL/pysatl-criterion repository, focusing on durable storage and efficient querying of statistical metrics. Leveraged Python and SQLAlchemy to design ORM models and implement a storage backend that supports both data persistence and retrieval. The work included targeted code cleanup, specifically removing obsolete commented code from the storage class to reduce technical debt and enhance maintainability. By establishing this core infrastructure, enabled future expansion of data-driven features while improving overall code quality. The approach demonstrated a strong emphasis on database design, ORM best practices, and maintainable Python development within a collaborative project.