Real, anonymous reports from people who interviewed for Data Engineer at Meta Logistics, newest first and distilled into what to expect across the loop.
After a fairly standard screening call, I moved into a technical conversation with a TA that felt noticeably harder than the prep materials. The challenge wasn’t that the underlying topics were impossibly advanced; it was that the problem statements were vague enough that I struggled to pin down exactly what the interviewer was expecting. I remember getting a setup that resembled a bookstore checkout flow, but the explanation around the intent and outputs was unclear, and the interviewer didn’t really help me resolve the ambiguity as I worked.
My process started with a screening step and then moved quickly into a LeetCode-style sequence focused on SQL and Python. The technical part felt like it ramped up from straightforward to harder questions, and at least part of the interview loop also had an architecture angle—around how the pieces fit together rather than just whether the code worked. Every technical round had SQL and Python in some form, and it kept coming back to building the solution end to end while keeping the logic straight.
The interview process begins with a recruiter call focused on fit and expectations, where candidates discuss their background and the role's requirements. This stage is generally conversational and sets the tone for the subsequent technical assessments.
Candidates undergo a timed technical assessment that includes multiple SQL and Python problems, often requiring them to explain their thought process while coding. The format is fast-paced, emphasizing both correctness and the ability to articulate reasoning under time constraints.
The technical rounds consistently feature SQL and Python, with questions ranging from basic syntax to complex logic involving business scenarios. Candidates should prepare for detailed SQL queries that require careful interpretation of data structures and relationships.
Onsite interviews include full-stack case study sessions where candidates must connect product goals to data modeling and SQL queries. This requires a strong understanding of both technical and product-oriented thinking, as interviewers probe the rationale behind decisions.
The interview loop concludes with behavioral rounds that assess candidates' ownership and thought processes in real-world scenarios. These rounds focus on how candidates approach problem-solving and their alignment with company values.
Candidates report a challenging experience overall, with tight time constraints and high expectations for correctness. Many felt that subtle nuances in answers significantly impacted their chances of receiving an offer, despite feeling prepared.