Procter & Gamble Data Engineer interview typically runs screening and behavioral rounds. It usually takes a few weeks and feels more like a screening-heavy process than a technical one.
$116K
Avg. Base Comp
$147K
Avg. Total Comp
2-3
Typical Rounds
1-2 weeks
Process Length
Our candidates report that Procter & Gamble’s data engineering interview can feel surprisingly detached from the work itself. In this case, the strongest signal wasn’t technical depth at all, but how the company probed for consistency under pressure through repetitive judgment checks and personality-style prompts. We’ve seen that kind of looping question pattern before: the same theme comes back in slightly different wording, which suggests they care less about a polished script and more about whether your answers stay stable when the framing changes.
A recurring theme is the presence of assessments that don’t map cleanly to the role, including a visual similarity task that felt unrelated to data engineering. That tells us P&G may be screening for broad cognitive fit and attention to detail, but candidates should not expect the interview to reward classic DE preparation in the way many other companies do. The non-obvious risk here is misreading the process as a technical bar when the actual evaluation seems to lean toward patience, composure, and how you respond to ambiguity.
For candidates, the key takeaway is that this process can feel more like a filter for temperament than for pipeline design or SQL fluency. We’ve seen frustration rise when people arrive expecting a conventional engineering conversation and instead get a series of abstract prompts. If you’re interviewing here, it helps to recognize that the company appears to value a very specific kind of steadiness — and that mismatch between expectation and evaluation is often what makes the experience feel so jarring.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Procter & Gamble process.
In my opinion, this was one of the most inane interview assessments I have ever sat through. Instead of anything that felt like a real data engineering interview, I was put through what felt like childish screening exercises and an exhausting stream of behavioral questions that kept circling back to the same themes in different wording, as if they were trying to catch me out rather than understand my experience. The strangest part was a question where I had to select whether two images were similar or not, which had nothing to do with the role I was applying for. That alone made the process feel completely disconnected from the job.
There wasn’t much of a technical signal to prepare for beyond that, which was honestly the most frustrating part. I went in expecting questions about pipelines, data modeling, SQL, or systems thinking, but the interview leaned heavily on vague judgment checks and repetitive personality-style prompts. The overall vibe was that they were more interested in testing patience than capability. I did not get an offer, and my main takeaway is that if you value your time, you should be ready for a process that may feel far more like a screening game than a serious evaluation of data engineering skills.
Prep tip from this candidate
Be prepared for repetitive behavioral questioning and at least one odd image-comparison style screening task. Don’t assume the process will focus on data engineering fundamentals like SQL or pipelines, because that was not the emphasis here.
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Featured question at Procter & Gamble
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Synthesized from candidate reports. Individual experiences may vary.
The process appears to start with a screening-style assessment rather than a traditional technical interview. Based on the experience shared, this stage included repetitive behavioral and judgment questions, plus an unusual image-comparison task where the candidate had to decide whether two images were similar.
Candidates are then asked a long series of personality and behavioral questions that revisit the same themes in different ways. The interviewee described this as a heavy focus on consistency checks and vague judgment prompts, with little direct connection to data engineering work.
There was no clear technical onsite or coding round described in the experience, and the candidate ultimately received a rejection. The overall process seemed to conclude after the screening and behavioral evaluation stages.