Walmart DevOps Engineer Interview Questions

“We had an outage after an engineer changed a config value directly in production. The first reaction in the room was to ask why he did that. I steered the review to a different question: why was it possible? We found there was no review step for config changes, and the safe path took so long that people skipped it under pressure. We moved config into version control with a quick review and made the safe path faster than the unsafe one. I tracked the action items weekly until they were closed, and the outcome was your result.”

“I noticed our test environments ran all night and all weekend even though nobody used them. I also saw several production services sized far above their real peak. For test systems, I added a schedule to shut them down after hours, with a simple way for anyone to turn one back on. For production, I reduced sizes in small steps, one service at a time, and watched error rates, latency and saturation for a full week after each step. One service showed higher latency, so I rolled that one back. The rest held steady, and the savings were your result.”

“I would start by agreeing with product teams on which paths must never fail, usually search, cart, checkout and pickup booking. Then I would load test those paths well above the forecast, because forecasts are often wrong on sale days. Each bottleneck we find gets fixed or gets a fallback. Closer to the day, I would freeze risky changes, scale up ahead of time, and prepare flags that turn off non-essential features like recommendations if the core paths get stressed. I would also run a practice drill where we break something on purpose, so the on-call team has already used the runbook once.”

“I would never push to every store at once. First a handful of pilot stores, then growing waves, with a pause between each to check health. Because store networks can drop, the device should download in the background, resume after a break, and only switch versions once the full package is verified. The old version stays on the device so it can roll back on its own if the new one fails a health check. Each device reports its version when it comes back online, so we can see progress store by store. I would schedule installs outside busy hours so staff are not interrupted.”

“I start from the shopper, not the server. For checkout, what matters is whether orders go through and how long it takes, so those become the main signals. For pickup, I would watch booking failures and whether store staff can see new orders. A page at night should mean shoppers are being hurt right now, measured against an agreed objective. High memory on one server goes to a dashboard or a ticket, not a phone. Every month I would look at which pages fired and whether anyone had to act; the ones that never need action get fixed or removed so people keep trusting the pager.”

“First I would remove long-lived deploy tokens and use short-lived credentials that only the pipeline can request, scoped to one job. Dependencies would be pinned to exact versions, scanned, and pulled through an internal mirror, so a bad upstream release cannot slip in overnight. Every build output gets signed, and the deploy step, including the updater on store devices, refuses anything without a valid signature. Changes to the pipeline itself need a second reviewer, because the pipeline is a target too. Finally, every release is logged with who approved it, which helps a lot during an investigation.”

“Most DevOps roles I have seen stop at the cloud. What interests me here is that the systems reach into physical stores, pickup counters and a supply chain, so reliability shows up in real places. If a release goes wrong, a store team or a shopper waiting for pickup feels it. I enjoy exactly that kind of problem: rolling out changes safely, planning for busy days, and running calm incident reviews. In my current role I built a staged deploy process, and I want to apply that thinking to a mix of cloud services and devices in stores.”

“The first move is to pause the rollout, so the problem does not spread to more stores. Then I would compare the list of failing stores with the stores that got the update. If they match, I would start a rollback for those stores and open an incident channel with support and the store operations contacts. Store staff need something to do now, so I would share a workaround, such as viewing pickup orders from another screen, if one exists. Once pickups are flowing, I would dig into why testing missed it, maybe a network condition the pilot stores did not have, and write it up.”