What to expect in a Zendesk data engineer interview: an online coding assessment, a technical round on Snowflake, dbt and SQL tuning, and a cultural fit conversation, based on one candidate's report.
$163K
Avg. Base Comp
$220K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Zendesk's data engineer interview is best prepared for as a stack-specific loop, not a generic coding screen. The one candidate report we have describes an online assessment, a technical interview and a cultural fit interview, and the technical conversation leaned on the data stack.
The candidate was asked about Snowflake and dbt, and also got a SQL tuning question: how would you optimize a slow query? That was described as the most practical part of the process and the closest to the day-to-day job. A sensible way to answer is to walk through checking the query plan, scan volume, joins, filters, and data layout before changing anything.
The online assessment was a single DSA-style problem that was implementation-heavy. The candidate felt the time limit was tight for the detail required and found the platform awkward. Practice writing complete, careful solutions under a timer rather than hunting for a clever trick.
This candidate was told at the start to expect six rounds, was then asked back for an extra round after what seemed like the final one, and ended with no offer. Treat the stated round count as flexible and ask your recruiter how many stages remain.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Zendesk process.
The process felt a lot longer than it needed to be. I started with an online assessment, then moved into a technical interview and a cultural fit interview, but the whole thing was framed at the beginning as six rounds with the last five supposedly technical. That already made me wary, and it only got worse when they asked me to come back for yet another round after what I thought was the final one. It honestly felt like they were still deciding while keeping candidates in the loop longer than necessary.
The OA itself was a single DSA-style question, but it was more about heavy implementation than anything especially clever. The problem was not that it was impossible, it was that the time given felt too low for the amount of detail they expected, and the platform had a lot of flaws that made it harder to work through than it should have been. In the technical conversation, they asked about Snowflake and dbt, so it was clearly geared toward the data stack rather than generic coding. I also got a SQL tuning question about how I would optimize a slow query, which was the most practical part of the interview and felt closest to the actual job. Overall the bar seemed reasonable, but the process itself was messy and frustrating. I ended up getting a no offer, and my main takeaway was to be ready for stack-specific questions around Snowflake, dbt, and SQL performance, while also not assuming the round count they mention is actually the final one.
Prep tip from this candidate
Be ready to explain how you would optimize a slow SQL query, and review Snowflake and dbt concepts since those came up directly. Also practice doing a fairly implementation-heavy OA under tight time limits, because the platform and timing were both part of the challenge.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Zendesk
How would you diagnose and speed up a slow SQL query when system metrics look healthy?
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Synthesized from candidate reports. Individual experiences may vary.
The candidate reports a single DSA-style question that was heavy on implementation rather than cleverness. They felt the time limit was low for the level of detail expected and found the assessment platform had flaws. Practice full implementations under a strict timer.
The candidate reports questions on Snowflake and dbt, plus a SQL tuning question about how to optimize a slow query. They described it as the most practical part of the process. Candidates may want to prepare to reason through query plans, joins, filters and data layout out loud.
The candidate reports a cultural fit interview alongside the technical conversation. No specific questions were reported, so applicants should typically prepare general examples of past data projects, collaboration and handling ambiguity.
The candidate was told at the start that the process had six rounds, with the last five technical. They were then asked to return for another round after what they believed was the last one. This is one report, so the number of stages may vary and is worth confirming with the recruiter.
