Robert Half's April survey found that AI fluency is now baseline in interviews, but this expectation also leads to slower, more complex hiring processes as candidates use AI for their applications.
u/Holiday_Lie_9435 — 19 days ago
u/Holiday_Lie_9435
u/Holiday_Lie_9435 — 23 days ago
Open-ended questions are common in data interviews to test how a candidate handles ambiguity, so it helps to have a framework that structures how you frame your answers, consider trade-offs, and justify your choices.
u/Holiday_Lie_9435 — 24 days ago
▲ 15 r/OfferEngineering
Sharing an interview experience here from a contractor who spent 3 years at Meta, but got rejected after the final loop for a full-time position.
Despite having internal experience, you will still be treated like an external candidate. So here’s more context on what happened.
The complete interview loop was 9 rounds. Structure was: 3 SQL + 3 Python + 3 data modeling challenges.
The biggest struggle was the Python rounds:
- Lack of prep since Python was used differently on the job
- Question style focused more on LeetCode-style logic problems, lists and dictionaries
Aside from the Python, the case study and behavioral round was hard.
Positioning was data analyst but actual strengths were more on data engineering. Doing the work =/= explaining the work, so it helps to practice packaging analysis into a more structured response.
If you’re also preparing for a data engineer role at Meta:
- Practice writing Python functions without pandas or numpy
- Prepare a format for answering case study/behavioral questions.
Also decide your positioning strategically between data analyst & data engineering roles; applying to both isn’t always practical without a clear story.
There’s a lot more detail in the full breakdown of this Meta interview experience. It includes the questions asked, how interviewers evaluate you, and more interview prep tips that can guide you for the full loop at Meta and other companies.
u/Holiday_Lie_9435 — 29 days ago