Agoda Data Engineer interview typically runs 3 rounds: online test, HR call, technical/system design. The process can move fast, but feedback may take up to two weeks and the rounds are time-pressured.
$130K
Avg. Base Comp
$130K
Avg. Total Comp
3-4
Typical Rounds
2-4 weeks
Process Length
We’ve seen Agoda lean hard on depth over breadth for Data Engineer candidates. Multiple candidates describe a process that starts with familiar SQL and Python, but the real signal comes from how quickly you can move from surface-level correctness to precise implementation detail. One candidate expected a broad system design conversation and instead got pressed on Apache Spark internals — especially persist, cache, and checkpointing — with the interviewer explicitly framing tool mastery as part of good design. That tells us Agoda is not just checking whether you know the concepts; they want to see whether you can reason from the mechanics of the stack itself.
A recurring theme is that the company seems comfortable making the interview feel tight and unforgiving. One candidate described three SQL questions in 20 minutes, with subqueries, calculations, conditions, concatenation, and naming logic layered on top of an ERD-heavy prompt. That combination suggests they care a lot about reading data models quickly and translating business logic into exact queries under pressure. The non-obvious trap here is that candidates who prepare only for generic data engineering themes may feel blindsided by how specific the evaluation gets. Our candidates report that the strongest performance comes from being fluent in the tools Agoda actually uses, not just in the broader discipline.
Synthetized from 2 candidates reports by our editorial team.
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Real interview reports from people who went through the Agoda process.
The hardest part for me was the third round, which was supposed to be system design on paper but quickly turned into a very tool-specific deep dive. I went in expecting a broader design discussion, but the interviewer kept drilling into Apache Spark internals and details like persist, cache, and checkpointing. I honestly found that frustrating because it felt like the expectation was to have very specific implementation knowledge memorized ahead of time, and that was never made clear upfront. The interviewer even said that only with enough tool depth can someone design a good system, which felt a bit harsh given the way the round was framed.
Before that, the process started with an online test covering Python and SQL. That part was straightforward enough for me to pass, and I got an email saying I would move on. After that, HR called to confirm my status and asked about visa needs, which was a quick check since I’m local and didn’t need sponsorship. Then, about a day later, I got the rejection email. The whole thing moved pretty fast once the test was done, but the feedback loop after interviews was slow, and I had to wait around two weeks at one point just to hear back. Overall, it felt like the process leaned much more on very specific Spark knowledge than on general data engineering thinking, so I’d definitely refresh those internals if you’re interviewing there.
Prep tip from this candidate
Refresh Apache Spark internals before the system design round, especially persist, cache, and checkpointing. Also be ready for an online test that includes both Python and SQL.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates complete a timed SQL and Python test. One experience described 3 SQL questions in 20 minutes, with queries involving subqueries, calculations, conditions, concatenation, and naming logic, making the pace quite strict.
After passing the assessment, HR reaches out to confirm candidate status and ask practical questions such as visa or sponsorship needs. In one case, this was a brief call before the next round was scheduled.
The technical round includes a system design discussion, but candidates reported that it can become a deep dive into Apache Spark internals rather than a broad architecture conversation. Topics mentioned included persist, cache, and checkpointing, with an emphasis on specific implementation knowledge.
After the interviews, candidates wait for the outcome by email. One experience noted a fast overall process after the test, but also mentioned a slow feedback loop during the interview phase, including a wait of about two weeks for updates.