SQL for Growth Analytics Questions
Domain specific SQL patterns used in growth analytics: cohort analysis, retention and churn calculations, funnel analysis for multi step user journeys, acquisition cohort queries, lifetime value computations, customer segmentation, and event aggregation for time series. Emphasis on USING GROUP BY, window functions, CTEs, date bucketing, cohort windows, and efficient joins against event tables to compute retention curves, conversion rates, and growth metrics at scale.
HardTechnical
0 practiced
Given extremely high-cardinality user properties in events (e.g., ad_id, device_model), explain SQL strategies to avoid exploding join cardinality when segmenting: include materialized user profiles, pre-aggregation, sampling, and hashed bucketing. Provide concise SQL patterns for pre-aggregating segment counts.
MediumTechnical
0 practiced
Write a SQL query that builds a 90-day retention matrix by weekly acquisition cohort: given events(user_id STRING, event_name STRING, event_time TIMESTAMP) and users(user_id STRING, signup_date DATE), produce output columns cohort_week, week_number (0..12), retained_users where week_number = 0 is cohort week. Use CTEs and window functions.
MediumTechnical
0 practiced
Describe how you would instrument and validate event properties (e.g., 'step' in a funnel) via SQL checks: write queries that detect missing/unknown property values, out-of-range timestamps, and improbable sequences (e.g., purchase before signup). Provide sample SQL checks.
MediumTechnical
0 practiced
Explain the trade-offs between using DENSE_RANK() and ROW_NUMBER() when identifying the first event per user if event_time can be identical across multiple rows. Provide a short SQL example showing when DENSE_RANK would return more than one row per partition.
EasyTechnical
0 practiced
Explain how INNER JOIN and LEFT JOIN behave differently when joining a users table (users.user_id) with a large events table (events.user_id) in growth analytics queries. Include one short example of when LEFT JOIN is required for correct cohort computation.
Unlock Full Question Bank
Get access to hundreds of SQL for Growth Analytics interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.