Data Engineering at Pinterest
Roles, Comp & Culture
An L4 mid data engineer at Pinterest sits around $377K total comp from 52 verified salary datapoints. The primary Data Engineering tech consists of Airflow, Flink and Presto, according to current job listings. Pinterest pays data engineers above other Technology companies. Reviews put them at 3.4 on Glassdoor, toward the bottom of the pack. Pinterest employees are under real strain and employee happiness is trending down over the past year. Layoff risk is elevated over the next 30 days. 1 data engineering role is open right now.
Technology · San Francisco, US
live data · August 2, 2026
DE total comp
$377K median
L4 · mid level · $291K–$502K · 52 verified datapoints
Hiring now
1 open DE role
live from career pages
Team happiness
Distressed
employee happiness
Layoff risk (30d)
Elevated
Employee sentiment
3.4 / 5Employees
51–200
Pinterest data engineer compensation
Each level's figure is the median of individual Pinterest offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
What the Pinterest signals mean
Pinterest's behavioral bar is less rigid than Amazon's Leadership Principles and less keeper-test than Netflix. Pragmatic decision-making is the central theme, and stories about choosing the simpler approach over the elegant one land especially well.
Pinterest employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
Recent Pinterest events
Layoffs, leadership changes, and other major moves at the company, with dates.
- Exec departureFeb 2026Leadership change
- LayoffJan 2026Layoff
- LayoffJan 2026~700 roles cut
- Exec departureSep 2025Leadership change
- Exec departureJan 2025Leadership change
Notable company events we track, with dates.
Pinterest data engineering tech stack
The languages, storage, and processing tools Pinterest data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
Pinterest data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Data engineering teams at Pinterest
Which team you interview for shapes the questions. The main DE surfaces:
Home Feed
Recommendation feature pipelines powering the discovery feed. Real-time and batch features, last-N-clicked-category signals, lifetime topic affinity, and the ranker that serves 500M+ users.
Search
Query understanding and retrieval features. Learning-to-rank signals, session context, and the feature engineering that connects a search intent to relevant pins.
Ads
Attribution and reporting. The 28-day click-window attribution pipeline, multi-touch weighting, advertiser-facing reporting, and the revenue fact tables that prove ads worked.
Trust and Safety
Graph and behavioral signal pipelines. Spam and abuse detection, signal aggregation per content_id from user reports, ML classifiers, and external feeds, plus moderation-console serving.
ML Platform
The in-house feature store (Galaxy), training and serving infrastructure, and point-in-time-correct training data. Shared across teams, so its patterns surface even in non-ML loops.
Analytics Engineering
Curated marts and metric definitions for finance and product analytics. Snowflake-centric, less recommendation-heavy, and the team most focused on data-model correctness and semantics.
What makes Pinterest different
The things about this company that should shape every answer you give.
Graph data shapes every system
Pinterest is a graph: pins on boards, boards owned by users, users following users and topics. Every system design and modeling answer should acknowledge the graph shape, even when the storage is relational. Frame queries as graph traversals, then explain the precomputed-aggregate optimization.
Ad attribution at scale is the platform tax
Pinterest's revenue is ads, and attribution is the system that proves ads worked. Every ad-related question implicitly checks whether you understand 28-day click windows, last-touch vs multi-touch, view-through attribution, and the privacy-driven shift away from third-party cookies.
Modern lakehouse plus legacy Hive coexistence
Pinterest's migration to Iceberg is real but incomplete. Some teams run pure Iceberg, others still on Hive. Your answer should be Iceberg-first but acknowledge the Hive-still-exists reality. Knowing the migration story is a plus.
ML platform questions show up in non-ML teams
Pinterest's feature store (Galaxy) and training infra are used across teams, including teams not formally on ML Platform. If you are interviewing for Home Feed or Ads, expect at least 1 feature-store question even if it is not your primary skill.
Preparing for the Pinterest loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Pinterest data engineer roles by level
Level-specific pages: the comp, the bar, and what the loop tests at each seniority.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Compare Pinterest with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Pinterest interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition