"ETL developer" was (and still is) the industry term for more than 10 years before this new wave of buzzword spam came along (i.e. when "data science" was "data mining"). Most of these roles focused on using low-code tools like SSIS to build business-focused data transformation workflows faster than with any code intensive approach (e.g. Hadoop ecosystem and derivatives).

Business intelligence consultant/developer was a blanket term used either for 1) people that can model and translate business requirements into data platforms/components; 2) poorly targeted recruiting; 3) more rarely, to describe people that work both in 'frontend' (reporting and dashboarding using tools like PowerBI, who are "data analysts" in newspeak) and 'backend' (ETL developer).

"Data Engineer" today is, sadly, too often synonym with "solving solved problems using an unnecessarily complex approach and toolkit"; and then there are the cases where the volume or complexity of the data actually justifies the cost of using "big data" platforms. Not to sound harsh; the article discussed here illustrates my point using "simple" unix tools: https://news.ycombinator.com/item?id=14401399