Agoda Data Engineer reports describe timed SQL work, an online Python-and-SQL test, an HR status check, and a third-round discussion that examined Apache Spark internals.
$133K
Avg. Base Comp
$160K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Candidate reports point to two preparation priorities for an Agoda Data Engineer interview: accurate SQL under time pressure and practical Apache Spark knowledge. One candidate described three SQL queries in 20 minutes. The prompts involved subqueries, calculations, conditions, concatenation, naming logic, and interpreting an ERD. They also said the platform did not allow syntax testing beforehand. Practice reading a schema quickly, deciding on the query structure, and writing a complete answer without relying on trial-and-error execution.
A separate candidate reported an online test covering Python and SQL, then a brief HR call about status and visa requirements after advancing. Their third round was framed as paper-based system design but became a detailed discussion of Spark persist, cache, and checkpointing. Prepare concise explanations of what each mechanism does, when it is useful, and the trade-offs behind the choice. It is also worth practicing how to connect those implementation details to decisions in a broader data-engineering design.
The reports do not establish a standard end-to-end timeline. One candidate mentioned waiting about two weeks for feedback at one point, while another report focused only on a timed assessment. Keep follow-up expectations flexible and concentrate preparation on the technical areas the accounts describe most clearly.
Synthesized from 2 candidate 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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Synthesized from candidate reports. Individual experiences may vary.
One candidate reported three SQL queries in 20 minutes. They described subqueries, calculations, conditions, concatenation, naming logic, and ERD interpretation, and said the platform did not allow advance syntax testing. Practice translating a schema and prompt into a complete SQL answer under a strict clock.
One candidate reported that their process began with an online test covering Python and SQL and that they advanced afterward. The account does not provide the question format or duration. Review both languages before the assessment, with particular attention to being ready for a mixed technical screen.
After the online test, the same candidate reported a brief HR call confirming their status and asking about visa needs. They characterized it as a quick check. Have a clear, accurate response ready about work authorization and any sponsorship requirements that apply to your situation.
The candidate described the third round as paper-based system design that became a detailed discussion of Apache Spark internals. They specifically named persist, cache, and checkpointing. Prepare to explain these choices clearly and relate them to the technical decisions you would make in a data-engineering design.
