In my team, "data engineer" refers to someone who deals with the ETL process, but also some feature engineering (ie, designing and extracting features. There's a lot of cross over here with the work Data Scientists do, but often the tools are different: for example, in the Python world the data engineer will use Pycharms while the data scientist will use Jupyter.
I'm sure someone can come along with a better description, but it's kinda like devops/sysadmin but specifically for data storage and access.
You could also look it up...
At some point, people realized that 90% of data science work was building pipes and keeping them clean. And then data engineering was invented so that the data scientists wouldn't have to get their hands as dirty. :)
A snappier version which gives the flavour: Data scientists use {tensor flow, scikit-learn, parquet, hadoop, spark, scrapers, etc}. Data engineers write {tensor flow, scikit-learn, parquet, hadoop, spark, scrapers, etc}.
I'd include database engineer in that bin. We used to call people DBAs but that seems a bit limiting now.
That's a good description for a data engineer from my end.