Interviewed at 5 companies in last 4 months , and I can tell data engineering interviews are most random and inconsistent..
It varied from someone asking hard leetcode in tech screen to asking to debug complex and poorly written codes.. even full fledged system design round was also happened.
And don't forget devops and cloud concepts..even they themselves are not using but they want you to know...Below is my experience
1) Google : leetcode plus system design plus SQL plus data modelling and distributed systems
2) Twitter : Anything random, just told me on-site will be related to data engg skills.. but asked 2 lc hard in my loop. One was related to research paper..Then some random questions on Docker..
3) Apple : All random, medium lc , but given case study to be completed with evaluators , code to be completed in spark or scala (no Python), and then switched gears to ML .. Though got the offer, but still thinking it's so tiring
4) Uber : API design. Failed because I have not done it and not even in my resume
5) Startup : homework assignment to modify web interface (what a shit!)I am not even a UI guy..
It was so tiring going through data engineering interviews..
In my experience, what I understand either you solve simple SQL for low paying data engg roles, or you must know leetcode , system design, data modelling, SQL, spark and some deep dive data engg. topics Programming constructs and other random topics
In that respect , I feel applying as software engineering roles make sense at least you grill lc and system design..And that should be it..
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Or you might end up interviewing with infrastructure team that handles lots of devops and cloud infrastructure
And may be just to show how great their team is may be they start asking random leetcode questions..
From my experience, companies like Lyft (not Uber), some fintech have quality interviewing process in DE space.