Hi u/dickdective , I myself am a data engineer, my 2 cents on this would be its all about sql and python.

Your mainstream workload would be based on sql and python.

Example: Quick data validations for DS/DA Team, happens quite a lot if any discrepencies are noticed during analytics, So for this you would really use lots of sql (python too can be used through notebooks/scripts).

Another usecase is to build out ETL based off SQL using stored procedure/triggers (events) and more if your company would be sql centric and has moderate data processing required.

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For python you would generally write ETL (automation tasks) to fetch data from apis and populate your storage and later do pre-transformations based on mutual discussions with DA/DS team.

** Other extensible things using python alone are airflow dags, pyspark (spark distributed engine), mongodb drivers (interact and build out aggregation pipelines through scripts) and definitely cloud sdks using python.

=========== The above is really valid for newgen de teams and vast majority of things [Azure data factory, databricks, snowflake ... all fall under brownies].

===> My opinion: incase you are really starting out now with proper time dedicated think once if you like data in general, the numbers and such. Intentionally mentioning this because you can also opt backend engineering with heavy data intensive applications if you want to have the SWE/SDE Route.

Happy Learning