Autodesk Data Engineer interviews reported by candidates typically run three to four rounds: a recruiter call, technical SQL/Python/pipeline questions or a take-home, and manager or behavioral conversations.
$120K
Avg. Base Comp
$162K
Avg. Total Comp
3-4 rounds
Typical Rounds
Not reported
Process Length
Candidates describe the Autodesk Data Engineer interview as a practical mix of SQL, Python and pipeline design, usually framed around how you reason rather than recalling answers. Reports describe three or four stages, typically starting with a recruiter call and ending with manager, behavioral or panel conversations.
On the technical side, SQL comes up repeatedly. Candidates mention joins, aggregations, window functions, deduplication, latest-record-per-key, sessionization and retention-style queries, with attention to performance such as filtering early and avoiding exploding joins. Python questions leaned toward maintainable pipeline code, parsing JSON and dictionaries, error handling and edge-case testing. One candidate also covered PySpark topics such as broadcast versus shuffle joins, skew, idempotent incremental loads and Parquet partitioning.
Design discussions varied. One candidate was asked to build a scalable ingestion pipeline while the interviewer added reliability and fault-tolerance constraints; another got a sparse prompt about alerting on unstable test jobs; a third modeled a Google Drive-style service. Clarifying inputs and outputs, stating assumptions and justifying keys and indexes were the common threads.
One candidate described a 24-hour take-home case study reviewed with the hiring manager. Another reported a two-month process, and another described slow recruiter follow-up, so ask for firm dates. Outcomes in the reports were mixed, including offers and rejections, so prepare for both the technical and the conversational rounds.
Synthesized from 7 candidate reports by our editorial team.
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Real interview reports from people who went through the Autodesk process.
The interview process at Autodesk for a Data Engineer role included four stages. A recruiter screen came first, followed by a take-home case study I had 24 hours to complete. After submitting, I had a review session with the hiring manager where we discussed my approach and the design tradeoffs I'd made. The final stage was two behavioral interviews back-to-back — consecutive conversations focused on my experience and how I approach challenges.
The technical rounds tested SQL, Python fundamentals, database management systems, ACID properties, and low-level system design. The take-home case study format felt fairer than a live coding interview; it allowed me to think through the problem carefully. I felt confident after each round.
Then came the rejection email. HR said someone else aligned better with their salary budget. I'd passed all four technical interview rounds and demonstrated clear technical competence, yet the deciding factor was compensation, not capability. It's demoralizing to clear every technical interview and still get rejected because you don't fit within their salary range. The technical performance wasn't the issue at all.
Prep tip from this candidate
Expect a 24-hour take-home case study reviewed with the hiring manager and back-to-back behavioral interviews. Focus your technical prep on SQL, Python, low-level system design, and ACID properties—these topics came up consistently across technical rounds.
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Sourced from candidate reports and verified by our team.
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 | |
| Deciding Between Solutions | |
| Scalable Data Pipelines | |
| Secure Messaging Platform | |
| Client Solution Pushback | |
| Data Cleaning Experiences | |
| 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 | |
| Minimum Change | |
| Random SQL Sample | |
| String Shift | |
| Customer Orders | |
| Average Quantity |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report a recruiter call as the first step, covering background and, in one case, what the candidate knew about Autodesk. It was described as straightforward. Prepare a concise project summary and ask about the remaining stages and expected timeline.
Candidates report SQL (joins, window functions, deduplication, retention), Python pipeline code and, for one candidate, PySpark and storage choices. Another described ETL, partitioning and late-arriving or duplicate data. One candidate instead received a 24-hour take-home case study reviewed with the hiring manager afterward.
Some candidates describe design conversations, such as a scalable ingestion pipeline with added reliability constraints, an alerting design for unstable test jobs, or a Google Drive-style data model. Interviewers may add constraints, so clarify inputs, outputs and assumptions, and explain key and index decisions out loud.
Later conversations reportedly focus on past projects, cross-functional work and conflicting priorities. Formats vary: a manager chat of about 25 minutes, back-to-back behavioral interviews, or a panel. Prepare a few specific stories about your own contributions.
