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Measurement Design and Analysis Questions

Practical measurement design and analytic techniques for producing reliable metric signals and proving impact. Includes instrumentation and tracking plans, experiment selection and validation, attribution modeling and its limitations, sample size and statistical considerations, identifying confounding variables, and reasoning about correlation versus causation. Also covers tradeoffs in data collection and data quality checks, cohort and segmentation design, baselining and threshold setting, designing dashboards and monitoring cadence, and connecting engineering and telemetry data to business outcomes. Candidates should be able to write clear measurement plans and success criteria, describe experiment and validation approaches, and explain how to operationalize results through reporting and iteration.

HardTechnical
28 practiced
Design an experiment and analysis plan to evaluate long conversion-window events (e.g., subscription renewals) where outcomes may occur months after exposure. Discuss using surrogate metrics, sequential analyses, censoring, and how to handle survivor bias.
MediumTechnical
56 practiced
Discuss how you would join engineering telemetry (e.g., event logs) with business tables (e.g., user profiles, billing) to measure downstream revenue impact. Address keys to join on, handling missing or inconsistent IDs, event latency, and strategies to reconcile differences between product and billing systems.
MediumTechnical
32 practiced
Design a cohort and segmentation strategy to analyze customer churn for a SaaS product. Describe how you'd choose cohort windows, segmentation axes (e.g., plan, usage), retention curves to plot, and how to ensure cohorts are comparable. Include suggestions for visualizations to communicate findings.
HardTechnical
33 practiced
Describe how to implement sequential testing with stopping rules that control Type I error (e.g., alpha spending, O'Brien-Fleming). Explain operational considerations for an experimentation platform that supports optional stopping and continuous monitoring.
MediumTechnical
39 practiced
You are asked to compute Daily Active Users (DAU) and 28-day retention cohorts from an events table. Given the schema:
events(user_id uuid, event_name text, occurred_at timestamp)
Write a Postgres SQL query (or describe queries) to:1) produce DAU per calendar day (UTC) with deduplication by user per day; 2) produce a cohort table where each cohort is users who first appeared in a given calendar week and show % retained at day 7, 14, and 28. State assumptions about timezones, deduplication, and users with multiple first-touch events.

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