Walmart Data Engineer interview typically runs 5 rounds: 1 OA, 3 technical rounds, and a final HR round. Based on one report, it took about 1 interview cycle and included a hiring manager project discussion.
$143K
Avg. Base Comp
$180K
Avg. Total Comp
5
Typical Rounds
3-5 weeks
Process Length
Our candidates report that Walmart’s data engineering interviews lean harder on fundamentals than many expect. The first signal is that the bar is not just “can you solve a coding problem,” but whether you can explain how core structures behave under pressure. One candidate described a second technical conversation centered on DSA application, with discussion of red-black trees and other implementation details that felt “pretty tough,” which tells us the team is looking for real algorithmic fluency, not memorized patterns.
A recurring theme is that the early technical screens may look approachable, but the difficulty can rise quickly once the conversation shifts from solving to reasoning. We’ve seen that the company seems comfortable probing into data structure mechanics like insertion, deletion, and heap sort, so candidates who only prepare for surface-level LeetCode-style prompts can get caught off guard. The interview experience suggests Walmart values engineers who can move from textbook concepts to practical tradeoffs without losing precision.
We also see that the hiring manager conversation tends to pivot toward projects, which is a useful clue about what they want to validate at the end: whether your technical depth shows up in real work. In other words, Walmart appears to care about both implementation rigor and whether you can connect that rigor to production experience. Candidates who can speak concretely about the systems they built, while still handling deep algorithmic follow-ups, seem best aligned with this process.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Walmart process.
I had a wonderful experience during my Karat interview for Walmart's data engineering role. The interviewer was fantastic—incredibly positive and supportive, which really helped me feel at ease throughout. His calm demeanor made it easier to focus on the technical challenges rather than getting caught up in interview anxiety.
The process consisted of four rounds total, and I participated in one of them. The technical questions were very solution-oriented and required genuine thought—not just pattern matching from LeetCode. I was asked Python coding problems, SQL queries, and data engineering infrastructure questions. The format was screen-shared, and they made clear that code didn't need to compile; pseudocode and clear logic were what mattered. One question that stood out was about processing a 100GB file on a single machine running Spark with only 5GB of RAM available—the kind of constraint-based problem that really tests whether you understand distributed processing fundamentals versus just knowing syntax.
The questions covered SQL basics, Python fundamentals, and practical data engineering scenarios. What impressed me was that the interviewer genuinely wanted to understand my thought process and problem-solving approach rather than just checking boxes. Even though the process was rigorous, his professionalism and kindness made it feel collaborative rather than adversarial.
Prep tip from this candidate
Focus on resource-constrained data processing problems—specifically how Spark handles large files with limited RAM, and be ready to think through partitioning, serialization, and memory management. Practice pseudocode-level problem solving where you can articulate your approach clearly without worrying about syntax perfection.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Walmart
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Subscription Overlap | |
| Customer Orders | |
| Last Transaction | |
| Top 3 Users | |
| Cumulative Sales Since Last Restocking | |
| Over 100 Dollars | |
| Scrambled Tickets | |
| Groups of Anagrams | |
| Random Forest Explanation | |
| Valid Anagram | |
| Hurdles In Data Projects | |
| Reducing Error Margin | |
| Order Addresses | |
| P-value to a Layman | |
| Triplet Counting | |
| Black Friday Shopping Spree | |
| Using R Squared | |
| Seasonal Product Performance Analysis | |
| Longest Increasing Subsequence | |
| Type I and II Errors | |
| Bias vs. Variance Tradeoff | |
| Moving Window | |
| Highest Average | |
| Assumptions of Linear Regression | |
| Rider Discount | |
| Merchant Dashboard Design | |
| Deciding Between Solutions | |
| Check Matching Parentheses |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with an OA that includes LeetCode-style easy to medium questions. This round is used to screen core coding ability before moving into live interviews.
The first technical round is also LeetCode easy/medium level. Candidates are expected to solve coding problems live and demonstrate basic algorithmic fluency.
This round focuses more on DSA application and deeper data structures knowledge. Topics mentioned include red-black trees and other tougher conceptual questions.
The final technical round is with the hiring manager and centers on past projects. Candidates should be ready to discuss their data engineering experience and the technical details of what they built.
The process ends with an HR round after the technical interviews. This stage typically covers final coordination and any remaining non-technical discussion before a decision.
