Data Engineering at Autodesk
Roles, Comp & Culture
An L5 senior data engineer at Autodesk sits around $280K total comp from 16 verified salary datapoints. The primary Data Engineering tech consists of Airflow, AWS and dbt, according to current job listings. Autodesk pays data engineers above other Technology companies. Reviews put them at 4.0 on Glassdoor, a little above the middle of the pack. Autodesk employees are stressed and employee happiness is trending down over the past year. Layoff risk scores low for the next 30 days. 8 data engineering roles are open right now.
Autodesk data engineer compensation
Each level's figure is the median of individual Autodesk offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
Autodesk employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
Autodesk pays above other Technology companies, with $280K at L5, and that's where the straightforward part of the offer ends. The 4.0 Glassdoor rating is a little above the middle of the pack, but Blind sentiment runs mixed, and internal happiness is stressed and trending down. The tension here is one that comes up repeatedly in mature enterprise-software companies mid-transformation: the engineering culture is real, the comp is solid, but the org has been through enough restructuring that engineers report uncertainty about team direction and prioritization. What you get is a stable paycheck, genuine product complexity, and a tech stack that transfers well. What you give up is the clarity and momentum of a leaner org. This is a company where your experience will depend heavily on which product area and manager you land in.
Recent Autodesk events
Layoffs, leadership changes, and other major moves at the company, with dates.
3 tracked layoffs in the past 12 months, the most recent in Apr 2026, and 1 executive departure in the same window. Those numbers reflect a company still digesting a significant workforce reduction announced in early 2026 that cut roughly 1,350 roles globally. Hiring has continued, but selectively: 8 open data engineering roles across 5 cities, concentrated in Toronto, which suggests data engineering investment is tilting toward the Canadian engineering hub rather than spreading evenly. The low near-term layoff tier implies the acute phase has passed, but anyone joining now should expect an org still recalibrating headcount and ownership boundaries. The next twelve months are more likely to bring steady pipeline work than greenfield platform bets, which matters for how you frame your growth expectations going in.
- Exec departureApr 2026Leadership change
- LayoffApr 2026~104 roles cut
- LayoffJan 2026Layoff
- LayoffJan 2026~1,000 roles cut
- Exec departureJun 2025Leadership change
- Exec departureMay 2025Leadership change
- Exec departureApr 2025Leadership change
- M&AApr 2025Acquisition / merger
- Exec departureApr 2025Leadership change
- Exec departureFeb 2025Leadership change
- LayoffFeb 2025Layoff
- Exec departureJan 2025Leadership change
- Exec departureDec 2024Leadership change
Notable company events we track, with dates.
Autodesk data engineering tech stack
The languages, storage, and processing tools Autodesk data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
Autodesk's core data problem is connecting dozens of discrete product lines (AutoCAD, Revit, Fusion, BIM 360, and more) into a coherent picture of customer behavior, subscription health, and product usage at scale. The shift from perpetual licenses to cloud subscriptions over the past decade created enormous event volume: every design session, export, collaboration invite, and renewal flows through pipelines that feed finance, product analytics, and customer success. The stack (Airflow, AWS and dbt, with Python, SQL and PySpark) points to a shop where engineers own ingestion from SaaS products and internal services, build Airflow-orchestrated batch jobs, and handle real-time Kafka streams for product telemetry. Regulatory surface is moderate for a SaaS company, though government and construction verticals add procurement and data-residency constraints. The job shape skews toward reliability and modeling depth rather than raw scale heroics.
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FivetranAutodesk data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Architect and implement scale batch and streaming pipelines for large-scale product telemetry with low-latency, high-throughput data access
Architect and implement scale batch and streaming pipelines for large-scale product telemetry with low-latency, high-throughput data access that support LLMs and agentic workflows optimized for
Build analytics tools that use the data pipeline to provide applicable insights into employee experience, operational efficiency and other main performance metrics
Architect and build the core Growth Analytics data environment with Snowflake as the central platform
Architect and implement distributed systems that process millions of records across batch and real-time pipelines
Practice for the Autodesk loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
The salary data has only 2 visible levels, with 11 reports at mid and 6 at senior, which tells you something about where Autodesk actually hires data engineers: mostly mid and senior, with limited appetite for junior or staff-plus roles. Engineers who do well here tend to like cross-product complexity, are comfortable with ambiguity about roadmap, and don't need a scrappy growth environment to stay motivated. If you're coming from a hypergrowth startup looking for similar energy, this will feel slow. If you've been burned by instability and want a company where pipelines exist, customers are real, and the problems are genuinely interesting, Autodesk is worth a serious look. The screen focuses on Python and the loop gets into pipeline architecture, so prep your systems design for pipeline architecture specifically before the interview starts.
Preparing for the Autodesk loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare Autodesk with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Autodesk interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition