Autodesk Data Engineer candidates report SQL, Python, PySpark, data modeling, and open-ended pipeline design discussions, alongside project and behavioral conversation.
$128K
Avg. Base Comp
$155K
Avg. Total Comp
3 rounds
Typical Rounds
2-4 weeks
Process Length
Autodesk Data Engineer interviews reported here center on practical reasoning across SQL, pipeline engineering, and system design rather than memorized trivia. One candidate described joins, deduplication, aggregations, null handling, and window functions, including latest-record-per-key, sessionization, and retention-style queries. Be ready to explain both correctness and scale: filter early, avoid join explosion, and articulate why a partitioning or join choice fits the workload.
Python and PySpark preparation should connect maintainable transformation code with production concerns. Candidates discussed parsing data, dates and JSON, error handling, unit-test edge cases, DataFrame-versus-SQL choices, broadcast and shuffle joins, skew, caching, repartitioning, idempotent incremental processing, and Parquet partitioning. Use examples from your own projects to show how you made a pipeline reliable and efficient.
Design questions can be deliberately underspecified. Reports include an alerting system for unstable test jobs, a scalable ingestion pipeline with changing reliability and monitoring constraints, and a Google Drive-style data model. Start by clarifying inputs, outputs, assumptions, entities, relationships, and operational tradeoffs before proposing a solution. Candidates also report manager or behavioral discussion about prior projects, collaboration, and priorities. The sample is limited, so sequence and format may vary.
Synthesized from 4 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Autodesk process.
The interview focused squarely on practical data engineering fundamentals: SQL, Python, PySpark, and my project experience. The SQL portion emphasized correctness first. I discussed inner versus left joins, deduplication, aggregations, null handling, and window functions, including patterns such as finding the latest record for each key, sessionization, and retention-style queries. There was also a clear performance angle: I was expected to think about partition pruning, filtering early, and avoiding joins that unnecessarily explode the data.
For Python, the conversation was less about obscure language features and more about writing maintainable pipeline code. I talked through clean functions for parsing and transforming data, handling errors, working with dates and JSON, and using configuration-driven approaches. They also probed for a unit-testing mindset, especially which edge cases I would test.
The PySpark discussion covered when I would use the DataFrame API versus SQL, how I would choose between broadcast and shuffle joins, and the use of repartitioning, coalesce, caching, and window functions. I was also asked about skew, incremental and idempotent processing, and practical storage choices such as Parquet, partitioning strategies, and reading and writing data in S3 or HDFS. I received an offer. My main takeaway is to prepare to explain not only how to write a solution, but why it will remain correct and efficient at scale.
Prep tip from this candidate
Practice SQL patterns for latest-record-per-key, sessionization, and retention, then be ready to explain performance tradeoffs such as partition pruning and avoiding exploding joins. For Spark, review broadcast versus shuffle joins, skew, idempotent incremental pipelines, and Parquet partitioning.
Share your own interview experience to unlock all reports, or subscribe for full access.
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.
One candidate reported a straightforward recruiter call before later interviews. Prepare a concise account of your relevant data-engineering experience and confirm the role scope, because other candidates described slow recruiting movement or an inactive listing.
Candidates report technical discussion of SQL, Python, PySpark, and project work. Topics included joins, aggregations, window functions, data transformations, test cases, join strategy, skew, incremental processing, and storage or partitioning choices.
Candidates report open-ended system-design or data-modeling exercises, including monitoring unstable jobs, scalable ingestion, and a Google Drive-style schema. A final conversation may also mix behavioral questions with system design, project examples, and collaboration.
