Data Observability
Also written as Great Expectations, Data Quality Testing, Data Quality Monitoring, Monte Carlo Data, Soda Core
Automatically checking data as it flows through pipelines — is it late, are rows missing or duplicated, did a value suddenly jump — so problems are caught before a wrong number reaches a dashboard or a model. Great Expectations, Soda and dbt tests are common ways to write the checks; Monte Carlo is a well-known commercial platform.
Think of it like
Smoke alarms for the data: they don't stop the fire, but you hear about it before the board meeting.
Junior or senior?
Signals a mature data team.
Senior sounds like
Can name a real data incident their checks caught (or missed) and explain how they decided which tables were worth monitoring.
Ask them
“Tell me about a data problem that reached a dashboard before anyone noticed. What did you put in place afterwards?”