← Block (Square) Interview Insights
The LEFT JOIN part is what trips people up.
Start by clarifying the table schemas and the definition of 'last 7 days' (e.g., relative to current date or a fixed date). Then, use a LEFT JOIN from referrers to referred users and orders, applying conditional aggregation to count distinct referred users, count distinct buyers in the last 7 days, and sum revenue. Ensure referrers with zero buyers are included by using LEFT JOIN and COALESCE for nulls.
Pro tip: Always confirm the grain of the orders table and whether revenue should be summed per order or per user; also, consider timezone and date boundaries for the 7-day window to avoid off-by-one errors.
Ask about table structures, column names, and the exact definition of 'last 7 days' (e.g., rolling 7 days from today, or a specific date range). Confirm whether revenue is per order or per user.
Determine the set of referrers to include. Use a LEFT JOIN from the referrals table (or a distinct list of referrers) to ensure all referrers appear, even those with no referred users or orders.
Join referrals to orders on the referred user ID, and filter orders to the last 7 days. Use a LEFT JOIN to keep referrers with no orders in the window.
Group by referrer and compute: COUNT(DISTINCT referred_user_id) for total referred users, COUNT(DISTINCT CASE WHEN order_date >= ... THEN user_id END) for buyers in last 7 days, and SUM(CASE WHEN order_date >= ... THEN revenue ELSE 0 END) for revenue.
Use COALESCE to replace null counts and sums with 0. Validate results by checking edge cases (e.g., referrers with no referrals, no orders, or orders outside the window).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by identifying the base set of users from the referrals table (referred_user_id), then LEFT JOIN to the orders table on user_id and filter for orders placed on or before today. Use GROUP BY on the user and HAVING COUNT(orders.id) = 0 to isolate users with zero qualifying orders.
Pro tip: Clarify the date boundary: 'on or before today' means order_date <= CURRENT_DATE, and consider timezone if orders have timestamps. Also, specify that you're counting distinct orders to avoid fan-out issues.
Select distinct referred_user_id from the referrals table to get the set of users who were referred.
LEFT JOIN the orders table on referred_user_id = orders.user_id AND order_date <= CURRENT_DATE to include only orders up to today.
GROUP BY referred_user_id and use HAVING COUNT(orders.id) = 0 to keep only users with no qualifying orders.
Check for NULLs, duplicate referrals, and timezone considerations; ensure the date condition is correctly applied in the JOIN rather than WHERE to preserve LEFT JOIN semantics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two layers of joins here: referrals to users for country, then a second left join to orders for the buyer flag.
Start by identifying the relevant tables and defining 'referred user' and 'bought something' precisely. Then write a SQL query that aggregates referred users per country, counts distinct buyers with purchase date <= today, and computes conversion rate. Finally, filter out countries with zero referred users but keep those with zero buyers, and round the conversion rate to two decimals.
Pro tip: Clarify the definition of 'referred user' and 'bought something' upfront—whether it's based on a referral event, a referral code, or a specific referral program, and whether 'bought' includes any purchase or only completed transactions. Also, consider time zones when filtering by 'today'.
Confirm what constitutes a referred user (e.g., users who signed up via a referral link) and what counts as a purchase (e.g., any completed transaction). Also clarify the date boundary for 'today' and whether it's based on UTC or local time.
Locate tables for users, referrals, purchases, and countries. Join them appropriately to associate each referred user with their country and any purchases they made.
Use GROUP BY country to count distinct referred users, count distinct users who made a purchase on or before today, and compute the conversion rate as buyers divided by referred users.
Exclude countries with zero referred users (e.g., HAVING COUNT(referred_user_id) > 0). Include countries with zero buyers (conversion rate 0.00). Round the conversion rate to two decimal places.
Check for edge cases such as NULL countries or duplicate referrals. Present the final result with country, referred_users, buyers, and conversion_rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one felt almost too practical for an interview but I actually think it's the most useful question in the set.
Start by writing a quick diagnostic query to list distinct country values and their counts, so you can see that the data uses 'GB' instead of 'UK'. Then explain that a naive WHERE country='UK' would silently return zero rows, leading to false conclusions, and show the corrected query using 'GB' with a comment documenting the discrepancy.
Pro tip: Always run a quick SELECT DISTINCT country (or GROUP BY with counts) before filtering on any categorical field—it takes seconds and prevents silent data loss. Also, mention that you'd flag the 'UK' vs 'GB' mismatch to the business to align on terminology and avoid future confusion.
Run a query like SELECT country, COUNT(*) FROM table GROUP BY country ORDER BY COUNT(*) DESC to see all distinct country codes and their frequencies.
Notice that 'GB' appears instead of 'UK', and confirm that 'UK' is absent from the results.
A WHERE country='UK' would return zero rows, which could be misinterpreted as 'no UK data' rather than a coding error, leading to incorrect business insights.
Use WHERE country='GB' and add a comment explaining the mapping (e.g., -- 'GB' is the ISO code for United Kingdom).
Re-run the query to confirm results, and inform stakeholders about the code discrepancy to prevent similar issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with SELECT * FROM users LIMIT 5 and SELECT * FROM referrals LIMIT 5, but in hindsight I'd ask for a COUNT(*) per table too, and maybe a check on whether referrer_user_id values actually exist in users (the sample has referrer 99 who isn't in users, which matters a lot for joins).
Start by explaining that your first two queries aim to understand the data's structure and quality, not to answer the main question yet. Propose a schema exploration query (e.g., listing tables/columns) and a sample data query (e.g., SELECT * LIMIT 10) to ground your subsequent analysis. Emphasize that this approach minimizes wasted effort and ensures you ask the right questions.
Pro tip: Mention that you'd ask for the row count and date range of key tables to quickly assess data volume and recency, which often reveals data pipeline issues or gaps. This shows you think about data reliability before diving into analysis.
Restate the main question and confirm what data is available. Acknowledge that you can only request queries, so you need to be strategic.
Ask for a query that lists all tables and their columns (e.g., using INFORMATION_SCHEMA). This reveals the data model and relationships.
Ask for SELECT * FROM [likely table] LIMIT 10 to see actual data values, formats, and potential quality issues.
Based on the results, refine your understanding and decide if additional exploratory queries are needed before the main analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.