Data Driven Analysis and Optimization Questions
Using data to diagnose problems, prioritize experiments, and drive optimizations. Includes clarifying metrics and goals, identifying and gathering relevant data, analyzing trends and anomalies, forming testable hypotheses, designing experiments such as A B tests, interpreting statistical significance, distinguishing correlation from causation, and recommending actions based on insights. Interviewers look for structured analytic workflows, comfort with basic statistics, and the ability to translate analysis into measurable product or operational improvements.
MediumTechnical
0 practiced
An analyst requests per-variant percentiles (10th, 50th, 90th) of session length for an experiment to understand distributional effects. Write an efficient SQL query (BigQuery/Postgres) to compute these percentiles per variant and discuss performance considerations and approximate alternatives for very large datasets.
EasyTechnical
0 practiced
Explain p-value, Type I and Type II errors in the context of A/B testing to a product manager. Use an example where releasing a change causes a Type I error and another where a Type II error causes missed opportunity. Also explain the trade-off between alpha and power in practice.
EasyTechnical
0 practiced
Explain the difference between correlation and causation. Provide three real-world examples where correlation might mislead product decisions and describe how a Data Engineer can help enable stronger causal inference (data collection, instrumentation, and experiment support).
HardTechnical
0 practiced
Describe how you would implement and validate an automated backfill strategy when a critical ETL job was down for 3 days and we need to recompute experiment metrics for that period. Include steps for idempotency, resource throttling, and verifying consistency between original and backfilled results.
MediumTechnical
0 practiced
A product manager asks you to implement a metric pipeline for 'time to first purchase' (days between signup and first purchase) aggregated weekly. Describe the ETL steps, the canonical tables you would create in the warehouse (with schemas), how you would handle users with no purchases, and how you would keep the metric backfillable when schema or logic changes.
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