Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Interviewed for a bizops role at Instacart and got a SQL question that seemed straightforward but required some careful thinking about how the data was structured across zip codes.

Questions Asked (1)

Write a SQL query to sum the total number of rides and deliveries completed, grouped by zip code, using the provided dataset.

Product Analytics & MetricsData Modeling

Author's notes

Felt pretty comfortable with the aggregation part but spent a weird amount of time second-guessing whether to use a single SUM with a CASE statement or two separate columns.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the dataset schema and the definitions of 'rides' and 'deliveries' (e.g., whether they are in separate tables or a single table with a type column). Then, write a SQL query that aggregates the counts of rides and deliveries per zip code, using appropriate JOINs or UNIONs as needed, and sum them to get the total.

Pro tip: Mention that you would validate the results by checking for NULL zip codes and ensuring that the sum of rides and deliveries matches the total number of records in the dataset. Also, consider indexing zip code for performance if the dataset is large.

1. Clarify the data model

Ask about the structure of the dataset: are rides and deliveries in separate tables or a single table with a type column? What are the relevant columns (e.g., zip_code, ride_id, delivery_id)?

2. Define the aggregation logic

Determine how to count rides and deliveries: use COUNT with appropriate filters or conditional aggregation (e.g., SUM(CASE WHEN type = 'ride' THEN 1 ELSE 0 END)).

3. Write the SQL query

Construct the query: if separate tables, use UNION ALL then GROUP BY zip_code; if single table, use conditional aggregation. Ensure to handle NULLs and use proper aliases.

4. Optimize and validate

Consider performance implications (indexes on zip_code) and validate results by cross-checking totals or sampling. Mention edge cases like zip codes with no rides/deliveries.

Key Points to Mention

Understanding the dataset schema and table relationships
Using conditional aggregation or UNION ALL based on data model
Handling NULL zip codes and ensuring data quality
Grouping by zip code and summing counts
Performance considerations (indexes, query optimization)
Validation of results (e.g., total counts match)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.