← Pinterest Interview Insights
Start by defining the cohort of users who signed up on or before each day D in August 2025. Then, for each day D, compute the number of users in that cohort who had at least one session in the 7-day window ending on D, and divide by the total cohort size. Use CTEs to organize the logic and window functions to handle the rolling window and cumulative counts.
Pro tip: Clarify whether '7-day rolling retention' means a rolling 7-day window (e.g., days D-6 to D) or a fixed 7-day period after signup; the former is more common for daily retention metrics. Also, consider edge cases like users with no sessions and ensure the denominator includes all users who signed up on or before D, not just those active.
Create a CTE with all calendar days in August 2025 to ensure every day is represented, even if there are no sessions or signups.
For each day D, determine the set of users who signed up on or before D. This can be done by joining the date spine with the users table and filtering signup_date <= D.
For each day D and each eligible user, check if they had at least one session in the 7-day window ending on D (D-6 to D). Use a window function or a self-join to aggregate sessions.
Divide the number of active users in the window by the total number of eligible users for each day D, yielding the 7-day rolling retention rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 'prove it via ROW_NUMBER' constraint is what makes this non-trivial.
Use a window function like ROW_NUMBER() partitioned by user and ordered by purchase date to identify the first purchase per user, then compute the days between signup and that first purchase. To prove the sum of orders strictly before the first purchase is zero, use a windowed SUM() over the same partition and order, checking that the cumulative sum up to the row before the first purchase is zero.
Pro tip: Explicitly state that you are avoiding MIN() subqueries to ensure a single table scan and better performance, and mention that the ROW_NUMBER approach is more scalable for large datasets like Pinterest's.
Use ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY purchase_date) to assign a row number to each purchase, where row number 1 indicates the first purchase.
Join the signup date and the first purchase date (where row number = 1) and calculate the date difference in days.
Use a windowed SUM() OVER (PARTITION BY user_id ORDER BY purchase_date ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) to compute the cumulative sum of orders before the current row, and verify it is zero for the first purchase row.
Combine the first purchase date, days to first purchase, and the zero-sum proof into a final result set, ensuring all users are included even if they have no purchases (using LEFT JOIN or equivalent).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt more straightforward than the retention part.
First, clarify the definitions and assumptions: what counts as an acquisition channel, how to attribute users to channels, and whether revenue is attributed to the user or the order. Then outline a SQL-based approach that joins user sessions, orders, and channel attribution within the specified window, computes ARPU per channel, and ranks the top 3.
Pro tip: Explicitly state that you would validate the data for edge cases like users with multiple channels or orders outside the window, and mention that you'd check for statistical significance or confidence intervals if the differences between channels are small.
Confirm what constitutes an acquisition channel (e.g., paid search, organic, referral), how users are attributed to channels (first-touch, last-touch), and whether revenue is attributed to the user or the order. Also clarify if ARPU is calculated per user or per session.
Locate tables for user sessions, orders, and channel attribution. Ensure you have fields like user_id, session_date, order_revenue, and channel. Confirm that the session window is August 26 to September 1, 2025, inclusive.
Filter sessions to the specified window, then join with orders on user_id and with channel attribution. Be careful to avoid double-counting revenue if a user has multiple orders or sessions.
For each channel, calculate total order revenue (sum of revenue from orders placed by users in that channel during the window) and divide by the number of unique users from that channel who had at least one session in the window.
Sort channels by ARPU descending and select the top 3. Present the results with clear labels and note any caveats or assumptions made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They wanted inline comments in the SQL explaining each.
Walk through the SQL script clause by clause (CTEs, JOINs, WHERE, GROUP BY, HAVING) and explicitly state where each edge case is handled, using defensive SQL patterns like LEFT JOINs, COALESCE, and date truncation. Emphasize that timezone normalization should happen early, ideally in a base CTE, so all downstream logic operates on UTC. Frame the answer as a design narrative that balances correctness with readability and performance.
Pro tip: Mention that you would add data quality checks or assertions (e.g., counts of users with no sessions) to validate edge case handling before trusting the results. This shows production maturity and prevents silent data issues.
In the first CTE, convert all timestamp columns to UTC using AT TIME ZONE or equivalent, ensuring consistent timezone handling. Document the assumption that source timestamps are in a known timezone (e.g., local or UTC) and state the conversion explicitly.
Use a LEFT JOIN from users to sessions so users with no sessions are retained, and filter signup_date <= analysis_end_date in the WHERE clause or a dedicated CTE to exclude users who signed up after the window. This ensures the denominator includes all eligible users.
Use a subquery or CTE with ROW_NUMBER() or GROUP BY user_id, DATE(session_ts) to collapse multiple events per day into one, or aggregate metrics (e.g., COUNT(DISTINCT session_id)) to avoid double-counting. Apply the same logic for orders.
In the final SELECT, filter events to the analysis window (e.g., session_ts BETWEEN start AND end), group by user and date as needed, and compute metrics. Use COALESCE to replace NULLs from LEFT JOINs with zeros for users with no activity.
Add a validation step (e.g., separate queries or comments) to confirm counts of users with no sessions, multiple sessions per day, and late signups are handled as expected. This demonstrates thoroughness and catches logic errors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.