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.
HardSystem Design
0 practiced
Design a scalable analytics schema and SQL patterns to compute rolling retention and cohort LTV at scale (1B events). Discuss partitioning, clustering, materialized views, pre-aggregations, and provide a short example SQL that builds a daily aggregate materialized table used by cohort queries.
HardTechnical
0 practiced
You have a retention SQL that joins events to itself per cohort and it runs too slowly. Describe step-by-step how you would optimize it: propose rewritten SQL patterns (e.g., using window functions or pre-aggregations), explain indexing/partitioning changes, and mention how to validate improvements using query plans.
EasyTechnical
0 practiced
Write a SQL query to compute median session length per user for the past 30 days. Given sessions(session_id STRING, user_id STRING, session_start TIMESTAMP, session_end TIMESTAMP). Return user_id and median_session_seconds. Use SQL functions available in standard warehouses (window functions/percentile_cont).
MediumTechnical
0 practiced
Case study: Product wants to compare retention between organic and paid acquisition channels controlling for signup week. Describe the analysis steps, SQL patterns (high-level), and how you would present results to stakeholders. Mention how you'd control for confounders.
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.
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