Pinterest Data Engineer Interview Guide

From hundreds of mock interviews, here's the pattern that catches Pinterest DE candidates off guard: they prep like it's a generic software engineering loop, when Pinterest's interview leans heavily on data pipeline design and Snowflake-specific SQL. Algorithms and data structures still matter (the role requires them, and multiple coding rounds test problem-solving proficiency), but candidates who only grind algorithm puzzles miss the infrastructure-heavy emphasis that makes this loop distinct.

Pinterest Data Engineer Role

Pinterest's data engineering org builds and maintains the pipelines that process pin engagement events flowing into a Snowflake-based analytics warehouse. You're partnering with ML and product teams who power recommendations and ads, but your job is the data infrastructure, not the models. Success after year one means owning a domain's pipelines end-to-end (say, ads attribution or creator analytics) and earning enough trust from cross-functional partners that they bring data requirements to you instead of building workarounds.

A Typical Week

The breakdown probably looks familiar if you've worked platform roles before, but the writing allocation is the number that should jump out. Design docs, migration proposals, runbook updates, on-call handoff notes: Pinterest DEs produce a surprising volume of written artifacts for an infrastructure role.

Projects & Impact Areas

The advertising data pipeline is where revenue lives. Pinterest is an ad-driven business, so if you're building the click-attribution join logic (the day-in-life data references a 28-day lookback window), your code directly affects the P&L. On the product side, the event ingestion layer captures pin saves, impressions, and closeups, feeding the Snowflake warehouse that downstream teams query for analytics and ML feature generation. Shopping and visual search pipelines are an emerging area too, connecting product catalog data to the discovery experience.

Skills & What's Expected

Communication scoring high is the detail most candidates overlook. Pinterest DEs present pipeline health dashboards and data quality metrics to product managers and ML engineers, not just other infrastructure folks, which is unusual for a DE role. ML knowledge, by contrast, is explicitly low-priority. Spend your prep time on Snowflake patterns (VARIANT columns, FLATTEN, semi-structured data handling) and Airflow DAG design, because that's what your actual workload centers on.

Levels & Career Growth

Most external hires land at L4 or L5. The real wall is L5 to L6: at L5 you own a domain like ads attribution data, while L6 demands driving cross-team technical initiatives such as writing the migration strategy that sunsets a legacy Hive pipeline org-wide. From what candidates report, the blocker at that boundary is rarely Snowflake or Spark skill. It's the ability to influence teams you don't report into through written proposals and stakeholder relationships.

Work Culture

Pinterest's PinFlex policy offers flexibility, though the source data indicates a hybrid model requiring three days per week in the San Francisco office, with most data engineering teams clustering Tuesday through Thursday in-person. The pace is sustainable: structured on-call rotations, rare crunch weeks, and most engineers working roughly 9:30 to 6. If you're coming from a company with mandatory five-day RTO, that three-day cadence is a real quality-of-life upgrade.

Pinterest Data Engineer Compensation

The vesting schedule can be uneven, and that changes everything. Pinterest RSUs vest over four years with a one-year cliff, but the annual weighting isn't always equal. Some offers use a front-loaded split (think 50/33/17 across the first three years, with the remainder in year four), which means your effective annual comp shifts significantly year to year. If you're comparing a Pinterest offer against another company's even-vest package, do the math for each individual year rather than averaging the grant across four.

The negotiation notes mention RSU count and sign-on bonus as movable pieces, and from what candidates report, equity tends to have more room than base salary. Pinterest competes for data engineering talent in a tight Bay Area market, so a competing offer strengthens your position. Focus your ask on the equity grant size and, if applicable, the sign-on bonus. Practice your numbers at datainterview.com/questions so you walk into the conversation knowing exactly what scope and impact story justifies the comp you're targeting.

Pinterest Data Engineer Interview Process

The System Design round is where most damage happens, and it's because Pinterest scopes it specifically to data platforms. You'll be asked to design something like a real-time analytics pipeline or a large-scale data warehouse, not a generic web service. Candidates who can't articulate tradeoffs around batch vs. streaming architectures, or who haven't thought through distributed storage and processing frameworks like Spark, Flink, and Kafka, tend to struggle here even if their coding rounds went well.

Don't sleep on the behavioral round just because it's the last hour of a long day. Pinterest's interview tips emphasize communication with both technical and non-technical stakeholders, and the role description explicitly calls out data governance and cross-functional collaboration. A flat behavioral performance, especially around ownership and stakeholder scenarios, can undermine an otherwise solid technical showing. Brush up on Snowflake-specific SQL patterns (VARIANT columns, FLATTEN, window functions) before the SQL & Data Modeling round, since that's the warehouse Pinterest actually uses.

Pinterest Data Engineer Interview Questions

from __future__ import annotations

from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple


@dataclass(frozen=True)
class Event:
    pin_id: int
    label: str
    ts: int  # Unix seconds, already sorted ascending


def longest_window_at_most_k_distinct_labels(
    events: List[Event], k: int
) -> Tuple[Optional[int], Optional[int], int]:
    """Return (start_ts, end_ts, length) for the longest contiguous window with <= k distinct labels.

    Notes:
      - Contiguous means a subarray in the given order.
      - If events is empty, returns (None, None, 0).
      - If k <= 0, returns (None, None, 0).
    """
    if not events or k <= 0:
        return (None, None, 0)

    freq: Dict[str, int] = {}
    distinct = 0

    best_len = 0
    best_l = 0
    best_r = -1

    l = 0
    for r, ev in enumerate(events):
        # Expand right
        if ev.label not in freq or freq[ev.label] == 0:
            freq[ev.label] = 1
            distinct += 1
        else:
            freq[ev.label] += 1

        # Shrink left until valid
        while distinct > k:
            left_label = events[l].label
            freq[left_label] -= 1
            if freq[left_label] == 0:
                distinct -= 1
            l += 1

        # Update best
        cur_len = r - l + 1
        if cur_len > best_len:
            best_len = cur_len
            best_l = l
            best_r = r

    return (events[best_l].ts, events[best_r].ts, best_len)

WITH filtered AS (
  SELECT
    user_id,
    pin_id,
    impression_ts,
    event_id,
    ingested_at
  FROM pin_impression_events
  WHERE impression_ts >= DATEADD('day', -7, CURRENT_TIMESTAMP())
),
ranked AS (
  SELECT
    user_id,
    pin_id,
    impression_ts,
    event_id,
    ingested_at,
    ROW_NUMBER() OVER (
      PARTITION BY user_id, pin_id, impression_ts
      ORDER BY ingested_at DESC, event_id DESC
    ) AS rn
  FROM filtered
)
SELECT
  DATE_TRUNC('day', impression_ts) AS impression_day,
  pin_id,
  COUNT(DISTINCT user_id) AS daily_unique_viewers
FROM ranked
WHERE rn = 1
GROUP BY 1, 2
ORDER BY 1 DESC, 2;

Pipeline engineering and system design questions at Pinterest aren't independent rounds so much as two lenses on the same problem: you might design a real-time ingestion layer for Pin engagement events in one session, then debug a backfill failure in that same layer during another. This overlap means your pipeline answers need architectural depth (why Kafka over direct Snowflake inserts for content moderation signals?) and your system design answers need operational specifics (how do you handle late-arriving events in the advertiser attribution pipeline?). From what candidates report, the most common prep mistake is treating this loop like a generic software engineering interview, spending weeks on algorithmic puzzles while barely practicing the Snowflake SQL patterns and pipeline design scenarios that together dominate the conversation.

Practice Pinterest-style questions across all six areas at datainterview.com/questions.

How to Prepare for Pinterest Data Engineer Interviews

Pinterest reported $4.2B in revenue for 2024, a 14.3% jump year over year, with nearly all of it coming from advertising. The company cut 15% of staff during its AI restructuring, yet overall headcount still grew 12.8%, which suggests the rebalancing favored technical roles, though Pinterest hasn't published a breakdown by function.

What does that mean for your prep? Read the Pinterest Engineering blog on Medium before anything else. Posts there describe how the team approaches real-time event processing for billions of daily pin interactions, and they'll give you concrete vocabulary for system design answers that sound like someone who's already thought about Pinterest's scale, not someone regurgitating a generic "design a feed" template.

The "why Pinterest" answer that falls flat is any variation of "I love the product's positivity." What works: reference a specific engineering challenge tied to Pinterest's push into social commerce and global ARPU growth. For example, talk about the data engineering complexity of connecting product catalog ingestion from thousands of retailers to the visual discovery surface, where schema inconsistencies across catalogs create real pipeline headaches that don't exist at a company like Snap or Meta. That's a problem only Pinterest's data engineers solve at this intersection of commerce and visual search.

Try a Real Interview Question

from typing import Iterable, Tuple


def count_deduped_events(events: Iterable[Tuple[str, str, int]], w: int) -> int:
    """Return the number of events kept after per-(user_id,event_id) deduplication within a w-second window.

    Args:
        events: Iterable of (user_id, event_id, ts) tuples; ts is an int seconds timestamp; events may be unsorted.
        w: Non-negative int window size in seconds. If ts - last_ts <= w for the same (user_id,event_id), treat as duplicate.

    Returns:
        Integer count of events kept after deduplication.
    """
    pass

from typing import Iterable, Tuple, Dict


def count_deduped_events(events: Iterable[Tuple[str, str, int]], w: int) -> int:
    """Return the number of events kept after per-(user_id,event_id) deduplication within a w-second window.

    An event (u, e, ts) is kept if there is no previously kept event with the same (u, e)
    such that ts - last_kept_ts <= w.

    This function sorts events by timestamp to ensure correct window semantics.

    Args:
        events: Iterable of (user_id, event_id, ts) tuples; ts is an int seconds timestamp; events may be unsorted.
        w: Non-negative int window size in seconds.

    Returns:
        Integer count of events kept after deduplication.
    """
    if w < 0:
        raise ValueError("w must be non-negative")

    events_list = list(events)
    if not events_list:
        return 0

    events_list.sort(key=lambda x: x[2])

    last_kept: Dict[Tuple[str, str], int] = {}
    kept = 0

    for user_id, event_id, ts in events_list:
        key = (user_id, event_id)
        prev = last_kept.get(key)
        if prev is None or ts - prev > w:
            kept += 1
            last_kept[key] = ts

    return kept

Pinterest's coding round skews toward problems where you need to reason about data quality and edge cases in messy input, not optimize time complexity on a classic algorithm. Practicing on event-log-style datasets at datainterview.com/coding will build that muscle faster than grinding pure algorithm problems.

Test Your Readiness

After you see your results, close the gaps with Pinterest-tailored questions at datainterview.com/questions. Pay extra attention to pipeline design and SQL, which together account for roughly 40% of the interview's weight.