[mrharrison]

I'm not comparing frontend to backend. I also think data is fun and I don't mean to be little the job, but in a real world scenario its detail intensive, under appreciated, tons of edge cases and extremely complex if you plan to make it scalable and fast. So if you are an aspiring data engineer be aware of these pitfalls, because the first couple times you do it you will think its fun to try something new and create some fun useful analytics, but customers will often complain at how long it takes and want more. It starts to wear away at ones drive and passion for data. Its not the data aspect its the job/deadline aspect.

[SmellTheGlove]

You're getting very close to the root cause - customers and even colleagues don't really care about the work that goes into the data. They care about the end deliverable, because that's what creates value for them, and fairly so. That gets at why data engineering as a discipline isn't (IMHO) very well respected.

I know this isn't reddit, so I'll point you to reddit. Check out /r/datascience where those folks talk about what it takes to be a data scientist. Some folks are honest about data engineering, but most handwave past it, or talk about it like it's beneath them. Their role would not be possible without solid data engineering, rather than a complementary and equally important discipline. Good luck doing "data science" or "analytics" or "machine learning" or every other buzzword without clean data, and for us data engineers, good luck ever demonstrating value without the analytics folks working with us.