Pinterest Data Analyst Interview Guide

Pinterest cut staff in recent years, yet its job postings tell a different story for data analysts: the company keeps hiring for teams focused on ads, monetization, and business operations. Candidates who prep exclusively for consumer engagement questions about the home feed often find themselves underprepared when the interview pivots to advertiser metrics and shopping conversion funnels.

Pinterest Data Analyst Role

This role lives at the intersection of Pinterest's advertising business and its Pinner experience. You'll split time between ads monetization work (auction mechanics, advertiser ROI, shopping ads conversion) and growth/engagement analysis (WAU trends, save rates, homefeed performance). Success after year one at Pinterest specifically looks like owning the metric definition for something like "Inspired Action Rate," a KPI the Commerce team uses to decide whether a new ranking signal is worth engineering investment.

A Typical Week

The mix here skews more toward communication than most candidates expect. You'll spend significant chunks of the week building exec-facing slide decks, drafting metric definition docs, and writing weekly analytics digests for stakeholders across Growth and Commerce. The deep SQL exploration happens in focused blocks, not all day, and a surprising amount of "analysis" time is actually Slack triage: quick queries to confirm whether a weekend dip in some cohort's engagement is real or a logging artifact.

Projects & Impact Areas

Shopping intent segmentation is a good example of the work: building cohorts of Pinners who move from browsing to clicking outbound product links, then feeding that analysis into how the Commerce team thinks about ranking signals. Experimentation ownership runs alongside that kind of project, where you'll design A/B tests for ad format changes and be expected to catch underpowered tests before results reach stakeholders. Revenue forecasting for quarterly business planning rounds out the portfolio, connecting your analysis directly to the numbers Pinterest shares on earnings calls.

Skills & What's Expected

Business acumen and data storytelling score highest in the skill profile, but don't underestimate the technical bar. SQL and data modeling (star schemas, event table structures) are medium-to-high requirements, not afterthoughts. Python with Pandas shows up for data cleaning in Jupyter notebooks, and Tableau is the primary BI tool. The real differentiator is your ability to translate a query result into a recommendation that changes how a product team acts, not just a chart that confirms what they already suspected.

Levels & Career Growth

The promotion from L4 to L5 is where most people stall. That jump demands you independently scope ambiguous problems and influence product roadmaps rather than executing on well-defined asks. L6 requires something qualitatively different: creating analytical frameworks that teams outside your own adopt.

Work Culture

PinFlex offers a choice between in-office, remote, or hybrid, which is unusually flexible for a public tech company. According to team culture notes, many analytics teams tend to cluster in the SF office midweek for stakeholder face time, though this isn't a rigid mandate. The pace is measured compared to hypergrowth startups, with most analysts working roughly 9:30 to 6 and no expectation of weekend hours.

Pinterest Data Analyst Compensation

Pinterest offers two RSU vesting structures, and which one you're on changes your comp trajectory dramatically. The main schedule is a 3-year front-loaded vest (50/33/17), which feels generous early but means your equity income drops meaningfully each year without new grants. Alternate options include a 4-year vest at 25% per year, either quarterly or with a 1-year cliff followed by monthly vesting, so make sure you understand exactly which structure is in your offer letter before signing.

Your biggest negotiation lever is the equity grant size, not base salary. The source data is clear that base and RSUs are the primary negotiable components, so anchor your counter there. If you have a competing written offer, use it to push on the RSU number and consider requesting a sign-on bonus to bridge any Year 1 gap from your current compensation.

Pinterest Data Analyst Interview Process

Expect roughly four weeks from recruiter screen to offer, though candidates report 1-2 week gaps of silence between rounds. The #1 rejection reason is weak SQL, not because candidates can't write queries, but because they write correct-but-slow ones and can't explain optimization tradeoffs when pressed.

From what candidates report, even rounds that feel conversational and positive can still result in rejection. Pinterest's process appears to weight written interviewer feedback heavily, and candidates on Blind have described getting turned down despite believing every round went well, with the stated reason being insufficient depth in a single area. Structure your answers so the substance stands on its own, independent of your delivery or rapport, because whoever evaluates that feedback may not have been in the room.

Pinterest Data Analyst Interview Questions

/*
Assumptions (typical ads billing model):
- delivery_hourly(ad_id, campaign_id, ts_hour, impressions, clicks, spend_usd)
- invoice_lines(invoice_id, line_id, ad_id, amount_usd, invoice_start_ts, invoice_end_ts, updated_at)
- invoice_lines can have duplicates for the same (invoice_id, line_id) due to updates, pick latest by updated_at.
- Allocate billed revenue to each day for an ad within the invoice period proportional to that day's spend.
*/

WITH params AS (
  SELECT
    DATEADD(day, -30, CURRENT_DATE) AS start_date,
    CURRENT_DATE AS end_date
),

-- 1) Delivery at daily grain (ad_id, dt)
delivery_daily AS (
  SELECT
    CAST(ts_hour AS DATE) AS dt,
    ad_id,
    SUM(impressions) AS impressions,
    SUM(clicks) AS clicks,
    SUM(spend_usd) AS spend_usd
  FROM delivery_hourly
  WHERE CAST(ts_hour AS DATE) >= (SELECT start_date FROM params)
    AND CAST(ts_hour AS DATE) <  (SELECT end_date FROM params)
  GROUP BY 1, 2
),

-- 2) Dedupe invoice lines to latest record per natural key
invoice_lines_dedup AS (
  SELECT
    invoice_id,
    line_id,
    ad_id,
    amount_usd,
    invoice_start_ts,
    invoice_end_ts
  FROM (
    SELECT
      il.*,
      ROW_NUMBER() OVER (
        PARTITION BY invoice_id, line_id
        ORDER BY updated_at DESC
      ) AS rn
    FROM invoice_lines il
    WHERE CAST(il.invoice_end_ts AS DATE) >= (SELECT start_date FROM params)
      AND CAST(il.invoice_start_ts AS DATE) <  (SELECT end_date FROM params)
  ) x
  WHERE rn = 1
),

-- 3) For each invoice line, compute daily spend within its invoice window
line_delivery AS (
  SELECT
    ild.invoice_id,
    ild.line_id,
    ild.ad_id,
    d.dt,
    d.spend_usd AS day_spend_usd,
    ild.amount_usd AS line_amount_usd
  FROM invoice_lines_dedup ild
  JOIN delivery_daily d
    ON d.ad_id = ild.ad_id
   AND d.dt >= CAST(ild.invoice_start_ts AS DATE)
   AND d.dt <  CAST(ild.invoice_end_ts AS DATE)
),

-- 4) Spend share per invoice line across its days
line_delivery_with_share AS (
  SELECT
    invoice_id,
    line_id,
    ad_id,
    dt,
    day_spend_usd,
    line_amount_usd,
    SUM(day_spend_usd) OVER (PARTITION BY invoice_id, line_id) AS line_total_spend_usd
  FROM line_delivery
),

-- 5) Allocate billed revenue to day, guard divide-by-zero (no delivery spend)
revenue_allocated AS (
  SELECT
    dt,
    ad_id,
    SUM(
      CASE
        WHEN line_total_spend_usd > 0 THEN line_amount_usd * (day_spend_usd / line_total_spend_usd)
        ELSE 0
      END
    ) AS billed_revenue_usd
  FROM line_delivery_with_share
  GROUP BY 1, 2
)

-- 6) Final KPI output at (dt, ad_id)
SELECT
  d.dt,
  d.ad_id,
  d.impressions,
  d.clicks,
  d.spend_usd,
  COALESCE(r.billed_revenue_usd, 0) AS billed_revenue_usd
FROM delivery_daily d
LEFT JOIN revenue_allocated r
  ON r.dt = d.dt
 AND r.ad_id = d.ad_id
ORDER BY d.dt, d.ad_id;

The distribution skews heavily toward ads monetization knowledge, not raw technical skill. SQL is the single largest category, sure, but product sense and experimentation together demand you reason about Pinterest-specific constructs like Promoted Pin auction dynamics, advertiser pacing ratios, and Shopping ads take rate, and a weak answer in one bleeds into the other since you can't design a sound A/B test for an ad format change if you picked the wrong success metric to begin with. The biggest prep mistake is treating SQL as the main event and assuming you can improvise on ads metrics. Over half the question weight requires you to think like someone who already works on Pinterest's monetization stack, not someone who just writes clean queries.

Practice these question types at datainterview.com/questions.

How to Prepare for Pinterest Data Analyst Interviews

Pinterest posted $4.2 billion in revenue with 14.3% year-over-year growth, and the company's stated bets tell you exactly where analyst work concentrates: increasing global ARPU, capturing social commerce share, and reallocating capital toward generative AI. That last point matters because the 2025 restructuring cut 15% of staff while simultaneously expanding headcount (up ~13% net), meaning the roles that survived are tightly aligned to monetization and AI-driven product innovation.

The "why Pinterest" answer most candidates fumble sounds like a mission statement remix: "I love visual discovery and helping people find inspiration." Instead, anchor your answer in the specific tension the business is navigating right now. Pinterest wants to diversify beyond standard display advertising into shoppable experiences while closing a stubborn international ARPU gap. Something like, "I want to help measure whether new commerce ad formats actually drive incremental conversions in markets where Pinterest monetizes a fraction of what it does in the US" shows you've done the homework.

Try a Real Interview Question

| advertiser_id | vertical |
|---------------|----------|
| 1             | Retail   |
| 2             | Retail   |
| 3             | Travel   |
| 4             | CPG      |

| spend_date  | advertiser_id | revenue |
|-------------|---------------|---------|
| 2023-01-15  | 1             | 100     |
| 2023-01-20  | 2             | 200     |
| 2024-01-10  | 1             | 150     |
| 2024-01-22  | 2             | 300     |
| 2023-02-05  | 3             | 80      |
| 2024-02-07  | 3             | 120     |
| 2023-01-08  | 4             | 50      |
| 2024-01-09  | 4             | 40      |

WITH monthly AS (
  SELECT
    date_trunc('month', s.spend_date) AS month_start,
    a.vertical,
    SUM(s.revenue) AS revenue
  FROM ad_spend_daily s
  JOIN advertisers a
    ON a.advertiser_id = s.advertiser_id
  WHERE s.spend_date >= DATE '2023-01-01'
    AND s.spend_date < DATE '2025-01-01'
  GROUP BY 1, 2
), paired AS (
  SELECT
    m24.month_start,
    m24.vertical,
    m24.revenue AS rev_2024,
    m23.revenue AS rev_2023
  FROM monthly m24
  JOIN monthly m23
    ON m23.vertical = m24.vertical
   AND m23.month_start = (m24.month_start - INTERVAL '1 year')
  WHERE m24.month_start >= DATE '2024-01-01'
    AND m24.month_start < DATE '2025-01-01'
)
SELECT
  month_start,
  vertical,
  rev_2024,
  rev_2023,
  (rev_2024 - rev_2023) * 1.0 / NULLIF(rev_2023, 0) AS yoy_growth
FROM paired
WHERE rev_2023 <> 0
ORDER BY month_start, vertical;

Pinterest runs two separate SQL rounds, and the second one specifically probes how you'd design schemas for ads and revenue event tables. Knowing your way around window functions isn't enough; you need to reason about why a table is structured a certain way and what breaks when event data is incomplete. Build that muscle at datainterview.com/coding.

Test Your Readiness

Drill ads-monetization framing and experiment design questions at datainterview.com/questions until you can talk about ARPU, commerce conversion, and guardrail metrics without pausing to think.