IBM Data Engineer candidates report technical questions built around their own projects, plus Spark, SQL, Python and warehouse versus data lake topics, followed by a manager conversation that touched on compensation.
$119K
Avg. Base Comp
$135K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Across the three IBM Data Engineer reports, the common thread is that interviewers lean on your own project work more than on pure algorithm drills. One candidate described a fast, direct technical interview tied to past work and personal responsibilities. Another was asked detailed questions about the data platforms they had used. A third was asked about the services in their toolset and how they handled a challenging project. Be ready to say exactly what you built, not just what the team did.
Topics differed by candidate. One report covered warehouse versus data lake trade-offs, Spark architecture and lazy evaluation, SQL, a basic Python string reversal, generative AI questions, and a maximum-length bitonic subarray problem. Another covered governance in the systems they built, scenario-based SQL, data modeling, and ETL and reporting, and noted that PySpark debugging can come up for Spark-aligned roles. The difficulty ranged from basic to uneven, with the bitonic problem the one clearly algorithmic item.
One candidate described a manager round that was less technical and included salary, which the team settled later. Another mentioned a managerial component where compensation could be negotiated. Either way, have a salary expectation ready.
Two candidates accepted offers and one reported no offer. One of them described scheduling problems, including a missed interviewer and a cancelled final round, in a business unit recently acquired by IBM. With only three reports, expect the format to vary by team and interviewer.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Ibm process.
Honestly, the technical round was pretty basic and much more focused on my own projects than on difficult DSA. I had one technical interview where the questions were tied to work I had already done and my previous experience. The conversation was fast-paced and direct, so I needed to explain what I had worked on, what my responsibilities were, and how my background matched the role. Nothing in my process felt like a heavy algorithm round, although the exact technical format at IBM can depend a lot on the interviewer.
After that, I had a manager round. This was less technical and included HR-style discussion about the role, responsibilities, and my experience. We also talked about salary. They did not finalize the compensation immediately during the interview; the team contacted me later with the amount they could provide. The overall process was straightforward, and the expectations for the position were explained clearly, including the emphasis on continuing to learn after joining.
I received and accepted the offer. My main takeaway is to know your projects properly because the technical discussion can stay very close to your resume. Be ready to explain your exact contribution instead of only giving a high-level project summary, and keep your compensation expectations ready for the manager discussion.
Prep tip from this candidate
Review every project on your resume and practice explaining your responsibilities, previous experience, and specific contribution, since the technical questions were based mainly on project work. Also prepare a clear salary expectation because compensation came up in the manager round and was finalized later.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Ibm
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Prime to N | |
| Largest Salary by Department | |
| Find the Missing Number | |
| Top 5 Turnover Risk | |
| The Brackets Problem | |
| String Mapping | |
| Valid Anagram | |
| Cyclic Detection | |
| Missing Housing Data | |
| Flatten JSON | |
| Find Duplicate Numbers in a List | |
| Target Indices | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| New Resumes | |
| Total Transactions | |
| Move Zeros Back | |
| Transformer Encoder Layer | |
| Swap Variables | |
| Binary Tree Conversion | |
| Slow SQL Query | |
| String Palindromes | |
| Targeted sum | |
| Equal Binary Subarrays | |
| Double Card Value | |
| Find Square Root | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Justify a Neural Network |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports starting with an introduction to their background before the conversation moved into projects. Expect to summarize your experience concisely, then be ready for follow-up on the data platforms and responsibilities behind each resume item.
Candidates report a technical round anchored in their own projects. Topics reported include warehouses versus data lakes, Spark architecture and lazy evaluation, scenario-based SQL, data modeling, governance and basic Python, and one report notes PySpark debugging can come up for Spark-aligned roles. One candidate also reported a bitonic subarray problem. Difficulty was described as basic to somewhat difficult.
One candidate describes a manager round that was less technical, covering the role, responsibilities and experience, with salary discussed and the amount shared by the team later. Another mentions a managerial component where compensation could be negotiated. Prepare a salary expectation and examples of how you keep learning new tools.
One candidate lists a PDMr round after the technical stage. Another says an HR stage is standard after the managerial discussion, but their final round was cancelled when an offer arrived. Final steps appear to differ by team, and scheduling problems were reported once, so confirm each appointment in writing.
