Pinterest Data Scientist SQL Interview Questions 2025

Preparing for SQL-focused interviews as a Pinterest Data Scientist in 2025? This guide synthesizes insights from industry trends and real-world examples to help you tackle technical challenges. Below, we break down common SQL question types, sample problems, and actionable strategies tailored to Pinterest’s data-driven ecosystem.

1. Core SQL Concepts Tested

Pinterest’s SQL interviews emphasize practical problem-solving aligned with their business needs, such as user behavior analysis, A/B testing, and recommendation systems. Key topics include:

• Complex Joins: Combining user engagement data (e.g., pins, saves) with ad performance metrics.
• Window Functions: Calculating rolling averages for user retention or session duration.
• Aggregation & Filtering: Summarizing metrics like daily active users (DAU) or click-through rates (CTR).
• Data Cleaning: Handling missing values or duplicates in large-scale datasets.

2. Example SQL Questions

Below are scenarios inspired by Pinterest’s data challenges, reflecting real-world interview problems:

A. User Retention Analysis

Task: Calculate the 7-day retention rate for users who signed up during a promotional campaign.

WITH signups AS (  
  SELECT user_id, signup_date  
  FROM users  
  WHERE campaign_id = '2025_promo'  
),  
activity AS (  
  SELECT user_id, DATE_TRUNC('day', activity_date) AS active_day  
  FROM user_actions  
  WHERE activity_date BETWEEN signup_date AND signup_date + INTERVAL '7 days'  
)  
SELECT  
  s.signup_date,  
  COUNT(DISTINCT s.user_id) AS total_signups,  
  COUNT(DISTINCT a.user_id) * 100.0 / COUNT(DISTINCT s.user_id) AS retention_rate  
FROM signups s  
LEFT JOIN activity a ON s.user_id = a.user_id  
GROUP BY 1;  ```

_Key Skills_: Date manipulation, left joins, and retention metric logic.

#### **B. A/B Test Evaluation**

**Task**: Compare CTR between two ad variants (A and B) and determine statistical significance.

SELECT
ad_variant,
COUNT(DISTINCT user_id) AS total_users,
SUM(clicks) AS total_clicks,
SUM(clicks) * 1.0 / COUNT(DISTINCT user_id) AS ctr
FROM ad_performance
WHERE experiment_id = ‘xyz’
GROUP BY 1; ```

Key Skills: Metric calculation, hypothesis testing basics, and aggregation.

C. Recommendation System Optimization

Task: Identify top 10% of users with the highest engagement (saves + clicks) in the last 30 days.

WITH user_engagement AS (  
  SELECT  
    user_id,  
    COUNT(save_id) + COUNT(click_id) AS total_engagement,  
    NTILE(10) OVER (ORDER BY COUNT(save_id) + COUNT(click_id) DESC) AS percentile  
  FROM user_interactions  
  WHERE interaction_date >= CURRENT_DATE - INTERVAL '30 days'  
  GROUP BY 1  
)  
SELECT user_id, total_engagement  
FROM user_engagement  
WHERE percentile = 1;  ```

_Key Skills_: Window functions (`NTILE`), engagement scoring, and performance optimization.


### **3. Interview Process & Preparation Tips**

#### **Interview Stages**

1. **Technical Screening**: Solve SQL problems on platforms like HackerRank or CodeSignal (e.g., debugging queries or writing joins).
2. **Onsite Rounds**:
    * **Live Coding**: Optimize queries for Pinterest-scale datasets (e.g., 2 trillion search logs).
    * **Case Studies**: Design SQL pipelines for scenarios like fraud detection or trend analysis.

#### **Preparation Strategies**

* **Master Window Functions**: Practice `RANK()`, `ROW_NUMBER()`, and `LAG()` for time-series analysis.
* **Study Pinterest’s Data Stack**: Familiarize yourself with tools like Spark, Hive, and Presto mentioned in their job descriptions.
* **Simulate Real Data**: Use public datasets (e.g., Google Analytics or social media logs) to replicate Pinterest’s use cases.


**Pinterest Data Scientist SQL Interview Questions 2025: Final Takeaways**  
Success requires balancing technical rigor with business acumen. Focus on query efficiency, metric design, and alignment with Pinterest’s mission to "bring everyone the inspiration to create a life they love." For deeper practice, explore platforms like LeetCode (tagged "Pinterest") or DataLemur’s SQL challenges.

**Pinterest Data Scientist SQL Interview Questions 2025** demand both precision and creativity—refine your skills with real-world datasets today!