Pinterest·Data Scientist·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Pinterest DS interview with a SQL/Python question centered on policy violation logs. The problem had a performance angle baked in which I didn't fully see coming.

Questions Asked (1)

Given a table of pin policy violations (with pin_id, violation type, and date), write SQL or Python to return all pin_ids that violated a specific policy type. Then extend your solution so that, given a policy type plus a date range, it retrieves matching pins efficiently using binary search instead of a full scan.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

I wrote the SQL part fine, basic filter on type.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the table schema and the specific policy type, then write a straightforward SQL query to filter violations. For the extension, propose sorting the data by date and using binary search to find the date range boundaries, then filter by policy type within that range. Discuss the trade-offs between SQL and Python implementations, and how to handle large datasets efficiently.

Pro tip: Mention that in a real-world Pinterest-scale system, you'd likely partition the data by date and use indexing to avoid full scans, but for the interview, demonstrate binary search on a sorted list to show algorithmic thinking.

1. Clarify requirements and schema

Ask about the table structure, data types, and whether the policy type is a parameter. Confirm if the date range is inclusive and if the data is sorted.

2. Write basic SQL query

For the first part, write a simple SELECT pin_id FROM violations WHERE violation_type = 'specific_type'. Mention that this is a full scan but acceptable for small data.

3. Extend to date range with binary search

Explain that to use binary search, the data must be sorted by date. In SQL, you can use BETWEEN with an index on date, but if implementing in Python, sort the list and use bisect to find start and end indices.

4. Implement binary search in Python

Show code: sort violations by date, use bisect_left and bisect_right to get the slice for the date range, then filter that slice by policy type. Discuss time complexity O(log n + k) where k is matches.

5. Discuss trade-offs and optimizations

Compare SQL vs Python approaches, mention indexing, partitioning, and that binary search requires sorted data. For very large data, consider distributed processing.

Key Points to Mention

SQL query with WHERE clause for policy type
Binary search requires sorted data by date
Use of bisect module in Python for binary search
Time complexity: O(log n) for search plus O(k) for filtering
Trade-offs: SQL indexes vs Python in-memory binary search
Handling inclusive/exclusive date ranges and edge cases

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