The funnel part was fine but the rolling window is where I got tripped up.
Break the problem into two independent aggregations: one for unique users with impressions per city/date from the events table, and one for completed orders per city/date from the orders table using the latest status per order. Then join these aggregates on city and date, compute the daily conversion rate, and finally apply a 7-day rolling window per city to get the rolling conversion rate.
Pro tip: Clarify the definition of 'completed orders' and how to handle orders with multiple status updates—using the latest status per order is key, but also consider timezone alignment between events and orders to avoid mismatched dates.
From the events table, filter for impression events, then count distinct users grouped by city and date. Ensure you handle any duplicate impressions per user per day.
From the orders table, first determine the latest status per order (e.g., using a window function or subquery), then filter for completed orders and count them grouped by city and date.
Join the two aggregates on city and date (using a full outer join to include days with only impressions or only orders), then compute conversion rate as completed orders divided by unique users with impressions, handling division by zero.
For each city, order by date and compute a rolling sum of completed orders and unique users over the past 7 days (including current day), then divide to get the rolling conversion rate. Ensure the window is correctly defined (e.g., ROWS BETWEEN 6 PRECEDING AND CURRENT ROW).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The NULLIF trick for division by zero I knew cold.
Start by clarifying the requirements: define the date window, what constitutes a non-cancelled order, and how to handle missing inventory dates. Then outline a step-by-step SQL or pandas approach that aggregates orders by supplier and date, joins with inventory data, and computes fill rate with proper null handling.
Pro tip: Mention that you would validate the results by checking edge cases, such as days with zero demand (fill rate undefined) and suppliers with no inventory records, to ensure the metric is robust and interpretable.
Confirm the date window, the definition of a non-cancelled order, and whether demand counts unique orders or unique order items. Also clarify if units_available is per supplier per date.
Filter orders to non-cancelled and within the date window, then group by supplier and order date to count unique orders (or order IDs) as demand.
Select supplier, date, and units_available from the inventory table. Ensure all dates in the window are represented, possibly by generating a date spine and left joining inventory, defaulting missing units_available to 0.
Left join demand with inventory on supplier and date. Compute fill rate as min(demand, units_available) / demand, handling cases where demand is 0 (e.g., set fill rate to NULL or 1).
Check for anomalies such as fill rates >1 or negative values. Discuss how to interpret fill rate and potential business implications.
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More conceptual than I expected at this point in the interview.
Clarify that the choice depends on the business question: demand should be attributed to the order creation date to reflect when customer intent occurred, while completed orders should be attributed to the completion date to reflect when revenue was realized. Justify by aligning with Lyft's operational and financial reporting needs, and acknowledge that both metrics serve different purposes.
Pro tip: Mention that you would align with finance and operations stakeholders to ensure consistency with revenue recognition and driver incentive periods, showing cross-functional awareness.
Ask whether the metric is used for demand forecasting, driver incentives, or financial reporting, as the appropriate date depends on the purpose.
Attribute demand to the order creation date because it captures when the customer requested a ride, which is critical for supply planning and marketing effectiveness.
Attribute completed orders to the completion date because revenue is recognized upon ride completion, and it reflects actual service delivery.
Explain that using creation date for demand helps predict future needs, while completion date for completed orders ensures accurate financial reporting and driver payouts.
Discuss handling of cancellations, long-duration rides, and time zones, and emphasize the need for consistent definitions across teams.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
ROW_NUMBER() partitioned by order_id ordered by status_updated_at descending, keep rank=1.
Start by clarifying the table schema and what 'latest status' means (e.g., by timestamp or version). Then write a SQL query using ROW_NUMBER() partitioned by order_id and ordered by the timestamp descending, filtering for row number 1. For the pandas part, replicate the same logic using sort_values and groupby().head(1) or drop_duplicates, then demonstrate rolling metric with rolling() and safe division using np.where or a lambda.
Pro tip: Always mention that you would validate the deduplication by checking for duplicate order_ids after the operation, and discuss the trade-offs between window functions and other methods (e.g., self-join) in terms of performance and readability.
Ask about the table structure, what defines 'latest' (timestamp, version, etc.), and whether there are ties. Confirm the expected output: one row per order with the latest status.
Use ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY updated_at DESC) to rank rows, then filter WHERE rn = 1. Alternatively, use RANK() or DENSE_RANK() if ties need special handling.
Sort the DataFrame by order_id and updated_at descending, then use groupby('order_id').head(1) or drop_duplicates(subset='order_id', keep='first'). Mention that sort_values is crucial for correctness.
Use df.rolling(window=...).mean() (or other aggregation) to compute a rolling metric. For safe division, use np.where(denominator != 0, numerator/denominator, 0) or a custom function to avoid division by zero.
Check that the result has unique order_ids and that the rolling metric behaves as expected. Discuss performance implications of window functions vs. pandas operations, and when to use each.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.