Communicate first: tell the stakeholders the dashboard is stale and give an expected time, because silence turns a data problem into a trust problem. Then find the first failing task rather than the loudest error, since downstream failures are usually symptoms. Decide between rerunning the failed slice and serving yesterday's data with a banner. Fix, rerun idempotently, verify row counts, then write up the cause.
Why interviewers ask this
This tests operational maturity and stakeholder handling together. Interviewers want communication early, triage from the top of the dependency graph rather than the bottom, and a decision about partial data. Candidates who only describe reading a stack trace miss that the business impact is being managed in parallel with the fix.
How to structure your answer
- Notify stakeholders immediately with impact and an ETA.
- Find the first failure in the DAG, not the last error message.
- Classify the cause: upstream data, infrastructure, or code change.
- Choose between rerun, partial serve, or hold with a stale banner.
- Verify the rerun with row counts and reconciliation before declaring it fixed.
Example answer
Before I debug anything I post in the stakeholder channel that the dashboard is showing stale data, what is affected, and when I will next update. That single message prevents about ten interruptions and keeps trust intact even if the fix takes two hours. Then I go to the top of the DAG, because in a chain of fifteen failed tasks, fourteen are just downstream of one real problem. Usually the real cause falls into three buckets: upstream data changed, infrastructure blipped, or someone merged a change. A quick look at whether anything deployed yesterday resolves that fast. Last time this happened to me the source system had shipped a column rename overnight with no notice, so the staging model failed on a missing field. I patched the model, reran the affected partitions, and because the pipeline is idempotent that was safe to do without cleanup. Then I verified row counts against the previous day before telling anyone it was fixed, and raised a contract conversation with the source team.
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See how it worksFollow-up questions to expect
- How would you decide whether to publish partial data?
- What would you put in place so a source schema change does not do this again?
- How do you communicate data freshness to dashboard users automatically?
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