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Pinterest·Data Scientist·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026Remote

Summary

Pinterest DS interview with a SQL-heavy technical screen. Two questions, both revolving around the same three tables, but the second one had enough moving parts that I was scribbling CTEs in my head the whole time they were still reading the prompt.

Questions Asked (2)

Q1

Given an impressions table with user, country, and category data, write SQL to return, for each country, the content category with the highest total impressions and its count.

Product Analytics & MetricsData Modeling
Author's notes

This part felt manageable.

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AI HintsAI Generated

Suggested Approach

Start by aggregating total impressions per country and category using GROUP BY. Then use a window function like ROW_NUMBER() or RANK() to identify the top category per country, and finally filter to return only the highest. Alternatively, use a correlated subquery or join with a derived table to get the max count per country.

Pro tip: Mention that you'd handle ties explicitly—either by using RANK() and filtering for rank=1 (which may return multiple rows) or by using ROW_NUMBER() with a deterministic tiebreaker like category name. This shows attention to edge cases and data quality.

1. Clarify the question and assumptions

Confirm what 'impressions' means (e.g., count of rows or sum of an impressions column) and whether ties should be broken. State any assumptions you make.

2. Aggregate impressions per country and category

Write a subquery or CTE that groups by country and category, summing or counting impressions to get total_impressions.

3. Rank categories within each country

Use a window function like ROW_NUMBER() or RANK() over (PARTITION BY country ORDER BY total_impressions DESC) to assign a rank to each category.

4. Filter to the top category per country

Select rows where the rank equals 1. If using RANK(), decide how to handle ties (e.g., return all tied categories or pick one arbitrarily).

5. Present the final query and explain

Show the complete SQL, walk through the logic, and mention any performance considerations or alternative approaches.

Key Points to Mention

  • Use of GROUP BY for aggregation and window functions for ranking
  • Handling ties: ROW_NUMBER vs RANK vs DENSE_RANK
  • Performance considerations: indexing on country and category, avoiding unnecessary subqueries
  • Assumptions about the impressions column (count vs sum)
  • Readability: using CTEs for clarity
  • Edge cases: countries with no impressions, null values

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

Q2

Using the same dataset, compute the weekly heavy-user rate, where a heavy user is defined as someone active on at least 4 distinct days in the last 7 days AND who used 3 or more distinct features on at least one of those active days.

Product Analytics & MetricsData Modeling
Author's notes

This one took me longer than I'd like to admit.

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AI HintsAI Generated

Suggested Approach

First, clarify the dataset schema and definitions (e.g., what constitutes an active day and a distinct feature). Then, compute per-user daily aggregates of distinct features, identify heavy users by applying the two conditions (≥4 active days in last 7 days and ≥1 day with ≥3 distinct features), and finally calculate the weekly heavy-user rate as the proportion of heavy users among all users in the week.

Pro tip: Always confirm the time window and whether 'last 7 days' refers to a rolling period or a fixed week; also discuss how to handle edge cases like users with no activity or incomplete data.

1. Clarify definitions and assumptions

Ensure you understand what 'active day' means (e.g., any event) and how to count distinct features. Confirm the time frame: is it a fixed calendar week or a rolling 7-day window?

2. Aggregate daily user activity

For each user and day, compute the number of distinct features used. This can be done by grouping by user and date and counting distinct feature IDs.

3. Identify heavy users

For each user, check if they have at least 4 active days in the last 7 days and at least one day where distinct features ≥3. Flag them as heavy users.

4. Compute weekly heavy-user rate

Divide the number of heavy users by the total number of users active in the week (or all users, depending on definition). Express as a percentage.

Key Points to Mention

  • Definition of 'active day' and 'distinct features' – clarify with interviewer if ambiguous.
  • Time window: fixed week vs. rolling 7 days; ensure consistency in calculation.
  • Handling of users with no activity or missing data – decide whether to include them in denominator.
  • Use of SQL window functions or group by for efficient computation.
  • Potential need to deduplicate events or features before counting.
  • Interpretation of the metric: what does a high/low heavy-user rate indicate for Pinterest?

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