We allow people to write pipeline tasks in .py files but open them as notebooks in Jupyter. So they keep the same workflow they're used to but under the hood, they're writing .py files - so they can do code reviews (jupytext handles the .py to .ipynb conversion). Also, when executing the pipeline, ploomber generates an output report for each script, so teams can use this to review any outputs generated by the code.
Finally, since the pipeline is modularized, it's easier to split the work. Some people may work in data cleaning, others in feature engineering, and they can all orchestrate the pipeline with "ploomber build".
You can read more about our approach in this guest blog post we published a few months ago on the Jupyter blog: https://blog.jupyter.org/ploomber-maintainable-and-collabora...
We'd love to hear from your experience! Please send me an email to eduardo@ploomber.io