The null delivered_at edge case is the part I almost missed.
Start by clarifying the schema and business rules, then outline a SQL-based solution using CTEs to filter eligible orders, compute delivery times, and aggregate daily metrics. Finally, apply a rolling 7-day window per city using window functions, ensuring correct handling of nulls and canceled orders.
Pro tip: Explicitly state your assumptions about the schema and edge cases (e.g., time zones, order status definitions) before diving into code; this shows you think like a data scientist who cares about data quality and stakeholder alignment.
Identify relevant tables (orders, deliveries, cities) and confirm definitions: on-time threshold, null handling, canceled orders, and time zone considerations.
Exclude canceled orders, join necessary tables, and compute delivery duration (delivered_at - created_at) for each order, marking on-time status.
Group by city and order date to calculate daily on-time count and total eligible orders, then compute the daily on-time rate.
Use window functions to compute rolling sums of on-time and total counts over the past 7 days per city, then derive the rolling rate.
Check for edge cases (e.g., cities with no orders on some days), ensure correct date alignment, and format the final output with city, date, counts, and rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tie-breaking in window functions always trips me up a bit.
Start by clarifying the exact definition of GMV and how to handle cancellations, then outline a two-part aggregation: one for GMV (excluding canceled orders) and one for cancellation counts (including all orders). Use a window function to rank merchants within each city, applying the tie-breakers, and finally filter to the top 3 per city.
Pro tip: Explicitly state that you would validate the results by checking edge cases, such as cities with fewer than 3 merchants or ties that require the secondary sort, and mention that you'd confirm the promo discount is subtracted from GMV (not added).
Confirm that GMV = subtotal + delivery fee + tip - promo discount, and that canceled orders are excluded from GMV but included in cancellation counts. Ask about the time zone and whether 'given month' refers to order date or delivery date.
Write a query that groups by city and merchant, summing GMV only for non-canceled orders, and counting all orders (or canceled orders) for the cancellation metric. Ensure the promo discount is applied correctly.
Use a window function like ROW_NUMBER() or RANK() with PARTITION BY city ORDER BY GMV DESC, cancellation_count ASC, merchant_id ASC. Explain the difference between ROW_NUMBER and RANK for ties.
Select only rows where the rank is <= 3. Validate by checking a few cities manually and ensuring no city has more than 3 merchants in the output.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a self-join or window functions to compare each order's 'delivered' event against its 'picked_up' events, filtering for orders where no 'picked_up' event occurs before the 'delivered' event. Then, for those orders, extract a minimal timeline showing the relevant events (e.g., the delivered event and any picked_up events that occur after or not at all).
Pro tip: Clarify the definition of 'strictly precedes'—if timestamps are equal, it does not count as preceding. Also, consider edge cases like multiple delivered events or missing picked_up events entirely, and mention how you'd handle them.
Examine the schema of the order events log table, including columns like order_id, event_type, and timestamp. Clarify that 'strictly precedes' means timestamp < delivered timestamp, and that we need orders with a 'delivered' event but no 'picked_up' event before it.
Filter the table for rows where event_type = 'delivered' to get a list of orders that have at least one delivered event. Note the delivered timestamp for each such order.
For each delivered order, check if there exists any 'picked_up' event with a timestamp strictly less than the delivered timestamp. Use a left join or NOT EXISTS subquery to find orders where no such picked_up event exists.
For the anomalous orders, select the delivered event and any picked_up events (if any) that occur after the delivered event or at the same time, to show the anomaly. If no picked_up events exist, just show the delivered event.
Double-check edge cases (e.g., multiple delivered events, null timestamps) and ensure the query returns the expected orders. Present the order ID and timeline clearly, explaining the anomaly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
ISO week start tripped me up for a second.
Start by clarifying the metric definition and edge cases, then outline a SQL query that joins orders and dasher assignments, filters out cancellations before acceptance, and computes the rate per dasher per ISO week. Emphasize correct handling of timestamps, week boundaries, and the denominator.
Pro tip: Mention that you would validate the metric by checking for anomalies like dashers with very low assigned order counts, and consider whether to set a minimum threshold for statistical significance. Also, note that timezone should be consistent (e.g., UTC) to avoid week boundary issues.
Confirm definitions: what constitutes an 'assigned' order, how to identify cancellations before acceptance, and whether to include orders with no dasher assignment. Discuss timezone handling for ISO weeks.
Locate tables containing order creation, assignment, acceptance, and cancellation timestamps. Ensure you have dasher_id, order_id, created_at, accepted_at, and cancellation status.
Filter orders to those assigned to a dasher and not canceled before acceptance. Compute time difference between accepted_at and created_at, flag those within 3 minutes, then group by dasher_id and ISO week of created_at.
Compute assigned order count, accepted-within-3m count, and rate as a decimal or percentage. Ensure ISO week start date is correctly derived (e.g., using DATE_TRUNC('week', created_at)).
Check for data quality issues, such as missing timestamps or negative time differences. Consider if the rate should be weighted or if minimum order counts are needed for reliable comparison.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward HAVING COUNT = 2 and COUNT(CASE WHEN canceled) = 2.
Use a two-step aggregation: first, count total orders and canceled orders per customer, then filter for customers with exactly two total orders and two canceled orders. Finally, retrieve the canceled_at timestamps for those customers and order them ascending.
Pro tip: Clarify whether 'lifetime orders' includes only completed orders or all orders, and confirm that 'canceled' refers to orders with a non-null canceled_at timestamp. Also, consider edge cases like customers with exactly two orders where both are canceled but one might have a null canceled_at due to data issues.
Identify the relevant tables (e.g., orders, customers) and columns (customer_id, order_id, canceled_at). Clarify definitions: what constitutes an order, and how cancellations are recorded.
Write a subquery to count total orders and canceled orders per customer. Use conditional aggregation (e.g., SUM(CASE WHEN canceled_at IS NOT NULL THEN 1 ELSE 0 END)) to get both counts in one pass.
Apply a HAVING clause to keep only customers where total_orders = 2 AND canceled_orders = 2. This ensures both orders were canceled.
Join back to the orders table for these customers to get the canceled_at timestamps. Use ORDER BY customer_id, canceled_at ASC to return them in ascending order per customer.
Check for duplicates, nulls, or unexpected results. Ensure the output includes customer_id and both canceled_at timestamps in ascending order, as requested.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The SQL part is a straightforward GROUP BY with a CASE WHEN promo_id IS NOT NULL split.
First, write a SQL query that aggregates orders by merchant and promo flag for the given month, computing counts and average order values, then join to get the difference. Next, explain how to compute a 95% confidence interval for the difference using either a SQL-based bootstrap (e.g., resampling with random numbers) or a Python snippet (e.g., using scipy or numpy).
Pro tip: Mention that you would check for sufficient sample size and consider using a t-test or bootstrap due to potential non-normality of order values. Also, note that you might need to handle merchants with zero promo or non-promo orders.
Confirm the definition of 'promo' (e.g., any discount applied) and the month, and assume order-level data with merchant_id, order_value, promo_flag, and order_date.
Use a query to group by merchant_id and promo_flag, filtering for the month, and compute COUNT(*) and AVG(order_value). Then pivot or self-join to get promo and non-promo counts and AOVs side by side.
Calculate the difference in AOVs (promo AOV - non-promo AOV). Exclude or flag merchants with zero orders in either group to avoid division by zero or unreliable estimates.
Describe resampling orders within each merchant and promo group with replacement, computing the difference in AOVs for each resample, and taking the 2.5th and 97.5th percentiles as the 95% CI. Mention implementing in SQL using random() or in Python with numpy/scipy.
Interpret the CI: if it excludes zero, the uplift is statistically significant. Note limitations like multiple testing, confounding, and the need for randomization for causal inference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.