Meta·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

SQL question for a Data Scientist role at Meta, pretty focused on aggregation logic with a join involved. Nothing too wild but the setup with two tables made it easy to overthink.

Questions Asked (1)

Given an Interactions table and a Products table, write a SQL query that returns each product along with its total interaction count summed across all buyers and sellers.

Product Analytics & MetricsData Modeling

Author's notes

My first instinct was to use COUNT(*) which would've been wrong since the interactions column already stores a pre-counted value.

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AI HintsAI Generated

Suggested Approach

First, clarify the schema of the Interactions table, especially how products are linked to interactions (e.g., via product_id) and whether interactions involve both buyers and sellers. Then, write a query that joins Products to Interactions on product_id and aggregates the interaction count per product using COUNT or SUM, ensuring all products are included even if they have zero interactions.

Pro tip: Mention that you would validate the grain of the Interactions table (e.g., one row per interaction) and consider using a LEFT JOIN to include products with no interactions, as this shows attention to data completeness and business impact.

1. Clarify the schema and requirements

Ask about the columns in Interactions and Products, how they relate, and whether 'total interaction count' means counting rows or summing a metric. Confirm if buyers and sellers are separate columns or roles.

2. Identify the join key and aggregation

Determine the join key (likely product_id) and decide on the aggregation function (e.g., COUNT(*) or SUM(interaction_count)). Ensure you group by product to get per-product totals.

3. Write the SQL query

Construct a query using LEFT JOIN from Products to Interactions to include all products, then GROUP BY product_id and aggregate the interaction count. Use COALESCE to handle NULLs for products with no interactions.

4. Validate and optimize

Check for duplicates, consider indexing on join keys, and test with sample data. Discuss potential performance implications for large datasets.

Key Points to Mention

Use of LEFT JOIN to ensure all products are returned, even those with zero interactions.
Aggregation function: COUNT(*) for counting interaction rows or SUM(interaction_count) if a count column exists.
Grouping by product_id and selecting product details (e.g., product_name).
Handling NULLs with COALESCE to display 0 for products with no interactions.
Consideration of buyers and sellers: if interactions are stored in separate columns, may need to sum both or use UNION ALL.
Performance considerations: indexing on product_id and avoiding unnecessary columns in GROUP BY.

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