Like most technology careers, the honest answer is: it depends on the company, the team, and the systems you are working with. Data engineering is not inherently stressful, but certain situations create pressure.
When data engineering gets stressful
Why experienced data engineers handle it well
The engineers who seem unruffled by production incidents are usually the ones who invested early in monitoring, alerting, and idempotent pipeline design. When you have set up proper alerting, you know about a pipeline failure before anyone else does. When your pipeline code is idempotent, you can safely re-run it without worrying about duplicate data. These are engineering choices that directly reduce operational stress.
Most of the "firefighting" that newer data engineers experience comes from inherited systems with no monitoring, fragile pipelines with no error handling, and ad-hoc code that nobody documented. In well-engineered teams, this kind of incident is rare.
How it compares to other tech roles
Data engineering is generally considered less stressful than customer-facing engineering or support roles because the work revolves around systems rather than people. There are no user tickets or on-call rotations in the traditional sense for most data engineering roles (though larger teams do have data on-call).
Most professionals who have been in the field for a few years describe the day-to-day as intellectually engaging rather than stressful — solving interesting data problems, building systems that scale, and having clear ownership over their pipelines. The compensation is good, the hours are reasonable at most companies, and the work accumulates into something tangible over time.