Autodesk Data Engineer interview typically runs 3 rounds: recruiter call, technical interview, final interview. It usually takes about 2-4 weeks and includes a conversational, follow-up-heavy final round.
$150K
Avg. Base Comp
$271K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Autodesk is looking for more than someone who can move data from A to B. The strongest signal in the experience we saw was the emphasis on pipeline design under changing requirements: one candidate described a system design conversation where the interviewer kept layering on reliability, monitoring, and fault-tolerance constraints to see how the solution evolved. That tells us Autodesk cares about engineers who can reason through tradeoffs in real time, not just present a polished architecture from memory.
We also see a clear pattern in the technical portion: SQL, Python, and core data engineering concepts are treated as a practical baseline, with questions around joins, aggregations, window functions, ETL design, partitioning, and messy-data scenarios like late-arriving or duplicate records. The non-obvious part is that these topics are not asked in isolation; they seem to be used to probe whether a candidate can connect data modeling choices to downstream reliability and usability. In other words, correctness plus operational judgment matters here.
The behavioral feedback reinforces that theme. Our candidate felt most confident when discussing past projects and cross-functional work, and the interview itself was described as more of a conversation about how problems are solved than a memorized checklist. That suggests Autodesk values engineers who can explain decisions clearly, handle pushback, and adapt when constraints change — especially in a product environment where data systems need to support many teams and use cases.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Autodesk process.
I had three rounds. The recruiter call was straightforward, then a technical interview focused on SQL, Python, and data engineering concepts, followed by a final interview that mixed system design and behavioral questions. I felt most confident discussing past projects and pipeline design. The system design round was the toughest because the interviewer kept adding new constraints and asking follow-up questions, so I definitely had to think on my feet. Overall, it felt more like a conversation about how I solve problems than a test of memorized answers.
Questions asked: From what I remember, the technical questions covered SQL, Python, and general data engineering concepts. I was asked to write SQL queries involving joins, aggregations, and window functions, along with a few Python questions on manipulating data structures. There were also questions about designing ETL pipelines, data modeling, partitioning, and how I would handle late-arriving or duplicate data. One system design prompt was to design a scalable data ingestion pipeline, and the interviewer kept adding constraints around reliability, monitoring, and fault tolerance to see how I would adapt my solution. The behavioral interview focused on past projects, working with cross-functional teams, and handling conflicting priorities.
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Topics based on recent interview experiences.
Featured question at Autodesk
Write a function to determine whether or not two rectangles overlap.
| Question | |
|---|---|
| Daily Retention Summary | |
| Hurdles In Data Projects | |
| Real-Time Hashtag Partitioning | |
| Priority Queue Using Linked List | |
| Cross-Region Inventory Sync | |
| Nearest Common Ancestor | |
| Addressing Data Quality Issues | |
| Scalable Data Pipelines | |
| Client Solution Pushback | |
| Parking Application System Design | |
| Flight Modeling | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Merge Sorted Lists | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Experiment Validity | |
| Download Facts | |
| Random SQL Sample | |
| Customer Orders | |
| String Shift | |
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| Top 3 Users | |
| Last Transaction | |
| Manager Team Sizes | |
| Month Over Month |
Synthesized from candidate reports. Individual experiences may vary.
A straightforward introductory call with the recruiter to discuss your background, interest in Autodesk, and fit for the Data Engineer role. This stage appears to be mostly a high-level screening before moving into technical interviews.
A technical round focused on SQL, Python, and core data engineering concepts. Expect SQL problems involving joins, aggregations, and window functions, plus Python questions on data structure manipulation and discussion of ETL pipelines, data modeling, partitioning, and handling late-arriving or duplicate data.
The final round combines system design and behavioral questions. You may be asked to design a scalable data ingestion pipeline and adapt it as the interviewer adds constraints around reliability, monitoring, and fault tolerance, along with questions about past projects, cross-functional collaboration, and handling conflicting priorities.