Illustrative scenario
Data analyst: explain the method behind the dashboard
The gap
A dashboard project shows outputs but omits metric definitions, data-quality checks and the decision it supported. Follow-ups reveal tool knowledge without analytical reasoning.
Target job requirements
Use SQL, data-quality checks and experiment analysis; communicate findings and uncertainty to business teams.
Repeated follow-up questions
- What are the metric denominator and unit of analysis?
- How would you detect and fix duplicated rows after joining orders and events?
- How would you explain an inconclusive experiment to stakeholders?
- How do you validate AI-generated SQL and avoid exposing private data?
Focused practice
Practice joins, window functions and experiment interpretation. Explain each analytical step using the actual business question and distinguish findings, inferences and recommendations.
What changes in this example
The scenario moves from “familiar with SQL and visualization” to explaining metrics, checking data and supporting decisions.
Practice this role