How would you approach designing a Python-based test automation framework for validating data transformations, and what specific tools or libraries would you use?

To design a Python test automation framework for data transformations, I would start by choosing a test runner such as PyTest, which supports parametrization and fixtures. I would structure the project into layers: a data layer that loads test inputs (CSV, JSON, Parquet) using pandas or pyarrow; a transformation layer that contains the functions or services to be tested; and a test layer that contains test cases. For assertions, I would use the built‑in assert statements or a library like assertpy for more readable syntax. I would add fixtures for setup and teardown, and use pytest‑mock to stub external dependencies. For reporting, I would integrate Allure or pytest‑html to generate readable reports. I would also add coverage measurement with coverage.py. Finally, I would set up a CI pipeline (GitHub Actions, GitLab CI, or Jenkins) to run the tests on every push and pull request.