Define the metric before explaining the trend
In a data analyst case interview, establish what a metric means before recommending a business action. Two dashboards can disagree because of definitions, scope or timing rather than an incorrect formula. Make those possibilities visible in your investigation.
This original case is for analyst applicants to English-speaking European teams. It tests requirement discovery and reconciliation rather than another list of SQL questions. The financial labels are fictional analysis inputs, not accounting or tax guidance.
Reconcile two fictional revenue views
A sales dashboard counts orders on their creation date. A finance view groups completed payments by settlement date and excludes refunded amounts. A manager asks which team has the correct monthly revenue number and whether a marketing campaign improved performance.
Clarify the decision the manager wants to make. Ask for the definitions, date boundaries, included records and treatment of refunds. Use a small sample of synthetic orders to show why a record can appear in one month in one view and another month in the other.
| Definition | Question to answer |
|---|---|
| Event | Order created or payment settled? |
| Date boundary | Which timezone and reporting period apply? |
| Record scope | Which states, refunds and exclusions are included? |
| Join and grain | One row per order, payment or another event? |
| Business question | Bookings, cash receipt or campaign-related behaviour? |
Explain why both views might be internally consistent
A useful answer is: 'I would not label either dashboard wrong until we agree the required metric. One view describes created orders and the other completed payments. I would reconcile sample records across the two event dates and document the definitions. Then I would select or build the view appropriate to the manager's decision.' This avoids settling the question by seniority or presentation quality.
For the campaign question, explain that a change over time alone does not establish causation. Identify what comparison and additional context would be needed. You can describe observed differences while leaving the causal claim unresolved.
Follow-up questions to test your reasoning
- Which sample record would explain the disagreement?
- What if one order has several payments?
- What evidence would support a campaign-effect claim?
Check whether the analysis preserves a consistent row grain and metric definition. A correct query can still answer the wrong business question. In practice, write the definition alongside the chart rather than leaving it only in a private note.
Explain the reconciliation without SQL first
Use three fictional orders with different event dates and one refund. Walk a partner through where each appears. Only then sketch the query or transformation. If the example is unclear before coding, the implementation will be harder to validate.
This is an original Cluegent practice exercise. Its scenario, example wording and suggested timings are illustrative, not an employer's assessment or scoring system. Use genuine experience in a real interview.
For optional preparation, paste the exercise into Cluegent as a typed prompt and ask for one follow-up at a time. Review the reasoning before adopting suggested wording. Redact personal and employer-confidential information. Try Cluegent on Windows or macOS; check the current trial and paid terms before downloading. Follow the employer's rules during an actual assessment.
Browse the Europe interview preparation collection or try Product manager prioritisation interview: a worked decision case.
Sources checked
These official references support the guide. Product details and technical documentation can change; check the linked source for current information.
Where Cluegent helps
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Frequently asked questions
How should I approach a data analyst case interview?
Clarify the decision, define the metrics and data grain, examine a small example and communicate what the evidence does and does not support.
Do different dashboard totals mean one is wrong?
Not necessarily. Check event definitions, time boundaries, scope and transformations before deciding.