Analytical Background Questions
The candidate's analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
EasyTechnical
0 practiced
Explain how data granularity affects metric calculations. Provide a concrete example where aggregating at the wrong grain (e.g., reporting at order-level vs customer-level) could lead to misleading conclusions such as Simpson's paradox.
EasyTechnical
0 practiced
List best practices when designing an executive dashboard for C-suite stakeholders. Include guidance on layout, KPI selection, color/visual encoding, interactivity, and one example of a misleading chart to avoid.
HardTechnical
0 practiced
Observational data may contain confounders. Explain methods to handle confounding when estimating causal effects: difference-in-differences, regression adjustment, instrumental variables, regression discontinuity, and matching. For each method state the key assumption and a scenario where it works best.
MediumSystem Design
0 practiced
Design a daily ETL pipeline to ingest sales events from an external vendor into the data warehouse. Include ingestion method, idempotency, handling late-arriving events, schema evolution, validation tests, and SLAs for freshness.
HardTechnical
0 practiced
You need to produce a 12-week revenue forecast for finance. Describe your modeling approach: data inputs and features, model classes you would consider, how you'd validate backtests with time-series cross-validation, how you'd present uncertainty to stakeholders, and how to deploy and monitor the model.
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