We also inspired from the same blog post ('Engineers Shouldn’t Write ETL') and built our own internal ETL tools.

Our primary design goal was the system to be self-service for data scientists. Since our data scientists use pandas dataframes and jupyter notebooks all the time, we built the system around these two: (1) We have a library (that we call pype) acting an interface between the database and python dataframes (similar to .to_csv method), so there is no SQL queries in ETL scripts, (2) schedule (parametrized) notebooks using some special keywords.

We have a demo screencast: https://drive.google.com/file/d/1SVTduaIH_3IsJ-QoGI4mLYZE8Jv...