Relevant DE Skills to Learn as a Transitioning Data Scientist?

My background is an MS in Data Science. I have taken a Data Engineering course in my Master's program, worked in cloud environment, performed ETL on a basic level (not fully automated), cleaned/prepared datasets, etc.

I want to learn DE from a practical perspective to be able to be a more well rounded data scientist, and my current employer doesn't have any dedicated DE, so I am currently filling that role.

- What are the most relevant tools/technologies I should focus on learning to help clients build a data infrastructure, ETL, process data, store data, etc.?

- What would be a good practical way to practice these skills with a particular datasets and cloud environment (AWS, GCP, Azure, etc.)?

- What are the core services/skills that you use most frequently as a DE that bring the most value to your clients/employers?