Instacart·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Instacart data scientist interview with a pretty gnarly SQL problem involving unpivoting a wide advertiser spend table and joining it to program financials. The question had four sub-tasks and felt more like a take-home than something you'd do live on a screen.

Questions Asked (2)

Given a denormalized advertiser spend table with one column per program, unpivot it into a tall format (advertiser_id, advertiser_name, program_code, spend) treating NULLs as zero, then find the single top-spending advertiser excluding those with zero total spend.

Data ModelingProduct Analytics & Metrics

Author's notes

The unpivot part was fine once I remembered UNION ALL is basically the safe fallback when the dialect doesn't support UNPIVOT natively.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the table schema and business context, then outline a two-step SQL approach: first unpivot the wide table into a tall format using UNION ALL or a lateral join, replacing NULLs with zeros; second, aggregate spend per advertiser, filter out zero-total advertisers, and select the top spender. Emphasize correctness, efficiency, and handling edge cases like ties or negative spend.

Pro tip: Mention that you would validate the unpivoted row count equals the number of advertisers times the number of programs, and that you would use COALESCE or IFNULL to handle NULLs early to avoid aggregation errors.

1. Clarify schema and requirements

Confirm the wide table's columns (advertiser_id, advertiser_name, and one column per program) and that NULLs represent zero spend. Ask about tie-breaking rules and whether negative spend is possible.

2. Unpivot to tall format

Use UNION ALL (or a lateral join/UNPIVOT function) to convert each program column into rows, applying COALESCE(program_col, 0) to treat NULLs as zero. Ensure advertiser_id and advertiser_name are carried through.

3. Aggregate and filter

Group by advertiser_id and advertiser_name, summing spend. Then filter out advertisers with total spend = 0 using a HAVING clause or a subquery.

4. Identify top spender

Order the filtered results by total spend descending and limit to 1. If ties are possible, clarify whether to return all tied advertisers or use a deterministic tie-breaker (e.g., lowest advertiser_id).

5. Validate and optimize

Check that the unpivoted row count matches expectations and consider performance implications (e.g., using UNION ALL vs. UNPIVOT, indexing). Discuss how to handle large datasets.

Key Points to Mention

Use of UNION ALL vs. UNPIVOT/LATERAL JOIN for unpivoting, and why UNION ALL is often more portable.
Handling NULLs with COALESCE or IFNULL to treat them as zero before aggregation.
Filtering out zero-total advertisers using HAVING SUM(spend) > 0 or a WHERE clause on a subquery.
Selecting the top spender with ORDER BY total_spend DESC LIMIT 1, and discussing tie-breaking strategies.
Validating the transformation by comparing row counts and spot-checking totals.
Performance considerations: avoiding unnecessary sorting, using appropriate data types, and indexing.

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

Using the unpivoted rows joined to a program financials table, identify the program with the highest net profit and the program with the highest profit margin. Return one row if they're the same program, two rows otherwise, with a reason column. Break ties by higher revenue then alphabetical program code.

Data ModelingTechnical Trade-offsProduct Analytics & Metrics

Author's notes

This is where I fumbled a bit.

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AI HintsAI Generated

Suggested Approach

First, compute net profit and profit margin per program by aggregating the unpivoted rows joined to the financials table, ensuring correct handling of revenue and cost components. Then, rank programs by net profit and profit margin separately, applying tie-breakers (higher revenue, then alphabetical program code) to identify the top program for each metric. Finally, compare the two programs and return one row if they are the same, otherwise two rows, with a reason column indicating which metric each program won.

Pro tip: Always clarify the definition of 'net profit' and 'profit margin' with the interviewer—whether net profit is revenue minus all costs, and whether margin is net profit divided by revenue—to avoid ambiguity and demonstrate business acumen.

1. Understand the data model

Examine the schema of the unpivoted rows and the program financials table to identify relevant columns such as program code, revenue, and cost components. Confirm how the join should be performed (e.g., on program code) and whether any filters or time periods apply.

2. Calculate metrics per program

Aggregate the joined data to compute total revenue and total costs per program, then derive net profit (revenue - costs) and profit margin (net profit / revenue). Ensure proper handling of NULLs or missing data.

3. Rank and select top programs

Use window functions or sorting to rank programs by net profit and by profit margin in descending order. Apply tie-breakers: higher revenue first, then alphabetical program code, to deterministically pick the top program for each metric.

4. Compare and format output

Compare the program with the highest net profit and the one with the highest profit margin. If they are the same, return one row; otherwise, return two rows. Include a reason column indicating 'highest net profit' or 'highest profit margin' (or both if same).

Key Points to Mention

Definition of net profit and profit margin: net profit = revenue - costs; profit margin = net profit / revenue.
Handling ties: use higher revenue as primary tie-breaker, then alphabetical program code for determinism.
Use of window functions (e.g., ROW_NUMBER, RANK) or ORDER BY with LIMIT to efficiently select top programs.
Ensuring correct join between unpivoted rows and financials table, possibly aggregating before joining to avoid duplication.
Output format: one row if same program, two rows otherwise, with a reason column.
Edge cases: programs with zero or negative revenue (margin undefined or infinite), missing data, and how to handle them.

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