The 2026 State of Data Engineering Survey (Interactive)
Thanks to all 1,001 of you for making this possible!
I've been collecting survey responses from this community for the past few weeks. 1,101 of you responded. Thank you!
Rather than bury the results in a 30-page PDF behind a email form, I built something different: an interactive explorer where you can query the data yourself.
👉 The Survey is here.
You can:
Filter by role, org size, industry, or region
Cross-tab any two variables
Run SQL queries directly against the datasetDownload the raw CSV
A few findings that stood out to me:
AI is table stakes. 82% of you use AI tools daily or more. Only 3.7% find them unhelpful. But organizational adoption lags way behind. 64% are still experimenting or using AI for tactical tasks only.
Modeling is painful. 59% cite "pressure to move fast" as their top pain point. 51% say lack of ownership. Only 11% say modeling is going well.
The bottlenecks aren't technical. Legacy systems top the list (25%), but lack of leadership direction (21%) and poor requirements (19%) are close behind. People problems rival tech debt.
Team outlook is cautiously optimistic. 42% expect growth, only 7% expect shrinkage.
The full report is in the explorer. But honestly, the interactive tools are more interesting. Go find something I missed.
Thanks for sharing the survey insights. The UI choices are really well done
Great insights! Agree on much of the findings. Many companies have foundational issues from pressure to 'move fast'. It is a tough sell to prove that foundational activities & first principles, are a flywheel for moving fast. Intentional data modeling, make data engineering faster, data quality better, and data consumption quicker and understandable. Also, from a few interns, finding more often the new generation of "Computer Science" graduates think analytic models are data models. ;( This is an educational system and training failure. As a data architect, when I hear the word 'building a model' in technical meetings, I often clarify, data or analytical? As data scientist spend 60-80% data schlepping vs. actual data science. End Rant... ;)