I started out my career as a 19 year old in business intelligence and old school data warehousing, and only in the last three years been able to properly apply my skills in the world of big data as a data engineer, and I've found that regardless of the fancy titles the kind of stuff I do is exactly the same. Perhaps the most surprising thing is that because "data engineering" is disassociated from the notion of traditional warehousing, you get lots of "experts" who have never heard of an ETL and think about software instead of data pipelines.

And with data scientists, I've found that it's a mix of a) people who did mathematics or physics degrees suddenly getting into computer science b) senior analysts learning how to power up their analysis and c) computer science graduates who went on to do phd in data science

Working with them my humble opinion is that a) you can't ignore the software aspect of your job, meaning that you need to understand basic database principles, parallel computing, SQL, etc. b) you also need to understand that it's not about how fancy your algorithm is, but also how it can be quantified, how you can manage the life cycle, how you maintain it, etc.