Pinterest Data Scientist Interview SQL Questions

TL;DR

Pinterest’s data scientist SQL interviews test event-based analytics, not just query correctness. You’ll face 3-4 SQL questions in a 45-minute round, often pulling from Pin engagement, user funnels, or ad performance datasets. The bar isn’t syntax—it’s translating business problems into efficient, nested queries.

Who This Is For

Mid-to-senior data scientists targeting Pinterest’s L4-L6 bands ($180K–$260K TC per Levels.fyi) who’ve cleared the recruiter screen but underestimate the depth of Pinterest’s event modeling. If you’ve only practiced LeetCode-style SQL, you’ll fail the debrief.

What SQL concepts does Pinterest emphasize in data scientist interviews?

Pinterest prioritizes window functions, date manipulation, and multi-table joins over recursive CTEs or advanced optimizations.

In a Q2 2023 debrief for an L5 candidate, the hiring manager noted the candidate’s query returned correct daily active users but missed the nuance of sessionization—Pinterest defines a session as 30+ minutes of inactivity, not calendar days. The candidate’s GROUP BY DATE(trunc) was technically right but business-wrong. The problem wasn’t the SQL; it was the lack of schema awareness. Pinterest’s event tables (e.g., pinimpressions, userengagements) are denormalized, so you’ll often need to self-join on userid and eventtimestamp ranges to reconstruct funnels.

Notably, Pinterest’s SQL rounds are less about writing the query from scratch and more about debugging or extending a partial query. A Glassdoor review from a 2024 L4 candidate described a prompt: “Given this query that calculates 7-day retention, modify it to exclude internal traffic.” The test was whether you’d add a WHERE clause for trafficsource != ‘internal’ or realize Pinterest’s internal users are flagged in a separate isemployee column. The latter is the correct signal.

How hard are Pinterest’s SQL questions compared to FAANG peers?

Pinterest’s SQL questions are easier than Google’s but harder than Meta’s in one dimension: business context specificity.

At Google, you might face a generic “find the second highest salary” to test edge cases. At Pinterest, the same concept appears as: “Find the second most engaged Pin for each user in the last 30 days, where engagement is defined as a weighted sum of saves, clicks, and closeups.” The query structure is identical (ROW_NUMBER() + partitioned ranking), but the business logic (weighted sum) trips up candidates who assume engagement = count().

A hiring committee debate from late 2023 highlighted this: A candidate with perfect syntax failed because they treated “engagement” as a binary (did the user interact?) rather than a spectrum. Pinterest’s data culture is metric-obsessed—your query must reflect that granularity. Meta’s SQL rounds, by contrast, often accept simpler interpretations because their funnels are less event-driven.

What are the most common Pinterest SQL interview questions?

You’ll see funnel analysis, retention, and A/B test queries—never abstract puzzles.

A recurring question: “Calculate the conversion rate from Pin impression to save, broken down by device type.” The trap isn’t the JOIN between impressions and saves tables; it’s realizing Pinterest’s impression table includes both organic and paid impressions, and the hiring manager expects you to ask whether to segment by adgroupid. In a 2024 debrief, a candidate lost points for not clarifying this upfront—the HC noted, “They wrote a correct query for the wrong problem.”

Another frequent prompt: “Find the top 3 Pins by 30-day rolling retention.” The naive approach (GROUP BY Pinid, DATE) fails because Pinterest’s retention is cohort-based. You need a self-join on userid with a date range (e.g., firstengagementdate + 30 days) and a window function to rank Pins. The problem isn’t the SQL complexity—it’s recognizing the time-bound nature of Pinterest’s product (a Pin’s virality decays fast).

How does Pinterest evaluate SQL answers in interviews?

They score on three axes: correctness, efficiency, and business alignment—not speed.

In a 2023 L5 interview, a candidate wrote a query that worked but used a CROSS JOIN where a simple LEFT JOIN would suffice. The interviewer didn’t dock points for syntax but asked, “How would this perform on a table with 100M rows?” The candidate’s pause revealed they hadn’t considered Pinterest’s scale. The debrief later noted: “Their SQL was functional, but their mental model of data volume was missing.” Pinterest’s datasets are smaller than Google’s but larger than most startups—expect tables in the 10M–1B row range.

Another evaluation criterion: whether you validate your results. A candidate who adds a LIMIT 10 to “test” their query signals laziness. Pinterest’s interviewers want to see you check edge cases (e.g., NULL handling in COALESCE, or time zones in DATE_TRUNC). In one case, a candidate’s query failed because they assumed UTC timestamps, but Pinterest’s user events are stored in local time (with a timezone column). The fix was trivial (add AT TIME ZONE), but the oversight cost them the round.

What dataset schemas should I expect in Pinterest’s SQL questions?

Assume three core tables: users, pins, and engagements, with denormalized event logs.

Pinterest’s engagements table is the most critical. It typically includes:

  • user_id (FK to users)
  • pin_id (FK to pins)
  • event_type (e.g., ‘impression’, ‘click’, ‘save’)
  • event_timestamp (with timezone)
  • device_type
  • traffic_source
  • is_employee (boolean)

A common schema trick: The pins table includes a createdat timestamp, but the “age” of a Pin in queries is calculated from the first impression, not creation. In a 2024 interview, a candidate joined pins on createdat and was marked down for not asking whether to use the first impression timestamp instead. The problem wasn’t the JOIN—it was the lack of schema curiosity.

Engagements are often pre-aggregated in Pinterest’s questions (e.g., “daily engagement counts”), but the raw table is what you’ll query. Expect to write subqueries to reconstruct funnels, like:

`sql

WITH first_impressions AS (

SELECT

user_id,

pin_id,

MIN(eventtimestamp) AS firstimpression_time

FROM engagements

WHERE event_type = 'impression'

GROUP BY 1, 2

)

-- Then join to saves, etc.

`

Preparation Checklist

  • Master window functions (ROWNUMBER, RANK, DENSERANK) for ranking and deduplication in event streams.
  • Practice date arithmetic (INTERVAL, DATE_TRUNC) with timezone-aware columns—Pinterest’s data is global.
  • Learn to self-join event tables to build funnels (e.g., impressions → clicks → saves).
  • Study Pinterest’s public metrics (e.g., MAU, engagement rates) to anticipate business context in queries.
  • Work through a structured preparation system (the PM Interview Playbook covers Pinterest’s event-based SQL patterns with real debrief examples).
  • Mock with Pinterest’s Glassdoor questions, but rewrite them to include edge cases (e.g., NULL traffic sources).
  • Time your queries: 15 minutes per problem is the de facto standard in Pinterest’s 45-minute round.

Mistakes to Avoid

  • BAD: Assuming engagement = count()
  • GOOD: Asking whether engagement is a binary event or a weighted metric (e.g., saves > clicks > impressions).
  • BAD: Ignoring time zones in timestamp comparisons
  • GOOD: Explicitly casting to UTC or local time (e.g., event_timestamp AT TIME ZONE 'UTC').
  • BAD: Writing a query that works on a sample but fails at scale (e.g., CROSS JOINs)
  • GOOD: Adding a comment like -- This JOIN could explode on 100M rows; consider a subquery to show awareness.

FAQ

Do Pinterest’s SQL questions include optimization problems?

No. Pinterest’s SQL interviews focus on correctness and business logic, not query tuning. You won’t be asked to rewrite a query for performance, but you will be penalized for inefficient patterns that reveal ignorance of scale.

Are Pinterest’s SQL questions take-home or live?

Live, in a shared doc (e.g., Google Docs or CoderPad). You’ll write queries against a schema description, not a real database. The interviewer may ask you to explain or modify your query mid-round.

Does Pinterest allow you to use a SQL cheat sheet?

No. But they don’t expect you to memorize syntax—focus on the logic. In a 2023 case, a candidate forgot the exact syntax for DATE_TRUNC but described the intent (“group by week”) and was prompted with the correct function. The debrief noted this as a positive signal.

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