Agoda Data Engineer candidates report a timed SQL or Python online test, an HR screen, and technical rounds that probe Spark and distributed computing depth, resume claims, and project experience.
$60K
Avg. Base Comp
$80K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Candidate reports for Agoda's Data Engineer interview vary, but the first hurdle is usually an online assessment. One candidate describes three SQL queries in 20 minutes, with subqueries, conditions, concatenation and an ERD to read, on a strict platform they could not try out in advance. Another reports an online test covering Python and SQL. A third was filtered by an SQL exam on Alooba before an HR screen. Practice writing correct SQL quickly from a schema.
The technical rounds lean on tool depth. One candidate who was rejected says a round billed as system design turned into a deep dive on Apache Spark internals such as persist, cache and checkpointing. Another, who received an offer, was asked about Hadoop, Spark and Elasticsearch, scaling problems, infrastructure design, and threads and concurrency, plus the projects they had shipped.
Resume scrutiny also appears. A candidate who went through an async video round, a live coding round and a mixed behavioral and technical round says interviewers drilled into skills listed on the resume, and they wished they had reviewed those more. The HR screen in another report was friendly and behavioral, covering strengths, weaknesses and a project challenge.
Timing varies. One candidate reports a first interview booked about three weeks after the test, and another waited about two weeks for a response. Expect uneven pacing.
Synthesized from 6 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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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 report an online test as the first filter. One describes three timed SQL queries in 20 minutes with an ERD, another a combined Python and SQL test, and another an Alooba SQL exam. Formats may differ, so practice reading schemas and writing accurate SQL quickly under time pressure.
Candidates report a recruiter or HR call after the test. Reported topics include self-introduction, how you heard about Agoda, strengths and weaknesses, a project challenge and visa needs. Prepare concrete project examples and honest answers rather than general claims.
Technical rounds appear to differ by candidate. One was asked about Hadoop, Spark, Elasticsearch, concurrency and past projects. Another went through a resume walkthrough and wrote code in a language of their choice. You should be ready to demonstrate every skill you list.
One candidate reports a third round framed as system design that became a Spark internals deep dive, covering persist, cache and checkpointing, and ended in rejection. Another, who received an offer, describes an informal chat with the team. Final rounds may therefore be either technical or conversational.
