But really, let the imposter syndrome go.
I'm a third-generation computer engineer and the first one with a degree. I'm half the engineer my father is or my grandfather was. Whatever title you put on your resume is just you marketing the services you deliver. If you can do the work of a Data Engineer, you're a Data Engineer. If you're really self-conscious about it, get a gcloud/aws certification.
The main advantage of using the data engineer label is that it describes what you do now so that others will understand. It doesn't describe your academic past, only your professional present.
What's more, titles like data/ML scientist/engineer are still too new and ill-defined for anyone to worry too much about the exact work implied or the underlying credentials. 'Engineer' is just an attempt to imply that the role involves more involvement in production than exploration.
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