The Organizational State of Data Engineering
A new pulse survey on what organizational dysfunction in data work actually looks like.
Hey everyone! I did three surveys so far in 2026, and 1,629 data professionals responded. One consistent theme: data engineering’s challenges are mostly organizational, and that’s the top bottleneck in data work.
In January, “leadership direction” and “poor requirements” combined for 40% of top-bottleneck votes, well ahead of legacy systems at 25%. In April, 50% of practitioners named “lack of clear ownership” as a top pain point, well ahead of better tooling at under 5%.
So what does “lack of leadership direction” actually look like at your company? Who owns the data products and infrastructure? How do requirements arrive - written spec, Slack DM, or reverse-engineered from a broken dashboard? Is AI making your organization function better, or worse?
The new survey takes about a minute. Anonymous. A handful of questions. The dataset will be open to the public when it closes, like all the others.
Survey closes Sunday, June 21 at 11:59pm PT. Findings published the following week.
Thank you for your support 🙏
Joe
Agreed. Getting alignment, extracting the requirements, known and unknown from the stakeholders, getting CTO on board, drawing up a plan, getting people to agree on plan, getting people to execute, making hard decisions … the easiest part has, and always will be, the code.
That’s an incredible survey on the organziational state of data engineering to understand the reality check with real gaps and challenges teams go through!!