Top 13 Data quality Interview Questions
Browse 13 real-world technical and behavioral interview questions about Data quality. Review scenarios, edge cases, and architectural best practices.
A small percentage of your transaction reports are rejected every day and the team resubmits them the next morning. Is that acceptable?
A steady rejection rate that is cleared daily is not a healthy control - it is a known defect being absorbed by people, and it says nothing about the reports that were accepted while being wrong. Rejections you can see are the smaller half of the problem.
A dashboard has been showing the same temperature for two days and nobody noticed. Walk the path and tell me what would have caught it.
On any protocol that reports by exception, no new value and an unchanged value look identical, so liveness has to be carried deliberately. Status codes, source timestamps, birth and death messages and a source-side counter each establish it at a different layer of the path.
An upstream team renames a field in the events you consume and nobody tells you. What should your pipeline have done?
Validate every record against a declared schema at the ingestion boundary, quarantine what fails instead of dropping it, treat additive changes as safe and renames as breaking, and alert on a field that has gone all-null rather than waiting for a dashboard to look wrong.
You are mapping data from a legacy system into a replacement and the two define a customer differently. How do you work that out?
Agree the business meaning and the grain of each entity before touching field names, profile the real data to find the overloaded and sentinel values, then write a mapping where every target field has a source, a rule, a default and a decision for records that cannot be mapped.
How do you decide when to retrain a model?
Retrain when there is evidence the learned relationship no longer holds and you have data that reflects the new one. A fixed schedule is a fallback for when you cannot measure that, not a decision, and a drift alert is as likely to mean an upstream pipeline broke as it is to mean the world moved.
Why does a bank end up knowing the same person as six different customers, and what does it take to give them one identity?
A golden customer record in banking is a derived identity view across product systems. Build it with scored entity resolution, source cross-references, attribute survivorship and reversible merge decisions.
How do you match patient records across systems when there is no shared identifier?
Patient matching across healthcare systems uses deterministic rules, probabilistic linkage and human review to connect records without a shared identifier. Blocking, thresholding and reversible links matter because false merges are clinically riskier than false splits. It also connects record linkage to the point an interviewer is testing.
How would you model products, variants and attributes for a catalogue that spans very different categories?
Separate the product a customer shops for from the variant that carries a barcode and stock, decide which attributes are variant-defining rather than merely descriptive, and hold the rest in a typed per-category schema, because a fixed column set cannot grow and an untyped bag cannot be trusted.
One machine's clock is ninety seconds ahead of the rest of the plant. Which of your numbers are wrong, and how would you have found out?
Every derived figure that joins on time is affected, and none of them error. Duration and attribution metrics take the skew directly, so a shift with twelve stops mis-measures availability by nearly four points, and only storing both event time and ingestion time makes the offset visible.
You've built a service catalog with an owner field on every entry. A year later, half the owners listed are people who left the company. How would you design the catalog so that doesn't happen?
Treat ownership as a derived, continuously verified fact rather than a manually entered field: source it from an existing system of record, expire it on a schedule, and fail builds or pages loudly when it goes unverified.
The business told you a field is always populated, and in production it is null for a third of the rows. How do you work out what is going on and what do you do about it?
Segment the nulls before drawing any conclusion — by date, source system, record type and creating process — because a null rate that clusters is a documented business path nobody mentioned, while one spread evenly is usually a genuine capture defect.
A supplier sends you a feed of ten thousand products. How many of them are already in your catalogue?
You cannot know without matching, and matching is the work: suppliers send their own identifiers, spell the same manufacturer three ways, and describe attributes in units you do not use. Treat ingestion as a matching problem with a confidence threshold and a review queue, not as an import.
Two dashboards report different revenue for the same month. How do you find out why, and how do you stop it happening again?
Reconcile the two numbers row by row before touching the pipelines: the cause is almost always a grain mismatch that fans out a join, a filter difference over refunds or test accounts, or two timestamps meaning different things. The fix is one certified definition that both dashboards are forced to call.