SQL Case Study Interview Questions and Answers (Updated in 2025)

SQL case study questions go beyond basic SQL queries, presenting hypothetical business scenarios that require you to define relevant metrics and write queries to investigate problems. These questions test your analytical thinking and SQL skills simultaneously.

To excel in SQL case study interviews, practice walking through solutions step-by-step. This approach helps you:

• Develop metrics for hypothetical scenarios
• Clearly communicate your logic to interviewers
• Demonstrate analytical problem-solving

Let’s explore two comprehensive SQL case study examples with detailed solutions.

Scenario: Twitter implemented a new notification system in an A/B test, resulting in increased unsubscribe rates.

Available Data:

• events table (user_id, created_at, action)
• variants table (user_id, experiment, variant)

Goal: Create a query to visualize how unsubscribes affect login rates over time.

Solution Approach

1. Make Assumptions

• We need to compare how the new notification system affects unsubscribes
• We want to analyze login rates before and after users unsubscribe
• We’ll compare results between control and test groups

2. Develop a Hypothesis

• Plot login rates on a graph with:
  • X-axis: days relative to unsubscribe (-30 to +30)
  • Y-axis: average login rate
  • Two lines representing control and test variants

3. SQL Implementation

SELECT 
    variant,
    DATEDIFF(e1.created_at, e2.created_at) AS days_since_unsub,
    ROUND(COUNT(DISTINCT CASE WHEN e2.action = 'login' 
    THEN 1 ELSE 0 END)/COUNT(DISTINCT abtest.user_id) * 100, 2) AS login_rate
FROM variants as abtest
INNER JOIN events AS e1
    ON abtest.user_id = e1.user_id
        AND e1.action = 'unsubscribe'
INNER JOIN events AS e2
    ON abtest.user_id = e2.user_id
        AND e2.action IN ('login', 'nologin')
        AND DATEDIFF(e1.created_at, e2.created_at) 
            BETWEEN -30 AND 30
GROUP BY 1,2

This course is designed to help you learn everything you need to know about working with data, from basic concepts to more advanced techniques.

If you have an interview coming up, review Interview Query’s data science course, which includes modules in SQL.

SQL interviews are demanding, and the more you practice all types of SQL interview questions and not just case questions, the more confident and efficient you’ll become in answering them.

Scenario: Analyzing career paths of data scientists to determine if frequent job changes lead to faster promotion to management roles.

Available Data:

• user_experiences table (id, user_id, title, company, start_date, end_date, is_current_role)

Goal: Prove or disprove the hypothesis that data scientists who switch jobs more often get promoted to management faster.

Solution Approach

1. Make Assumptions

• We need to segment data scientists based on job-switching frequency
• We’ll analyze the correlation between job switches and promotion rates

2. Develop a Hypothesis

• Analyze existing data science managers
• Calculate how many job switches they made before promotion
• Determine average time to promotion based on job switch frequency

3. SQL Implementation

WITH manager_promo as (
    SELECT user_id,
        MIN(start_date) as promotion_date
    FROM playground.user_experiences
    WHERE title='data science manager'
    GROUP BY user_id
)

SELECT num_jobs_switched,
    AVG(
        TIMESTAMPDIFF(MONTH, career_started_date, promotion_date)
    ) as month_to_promo
FROM (
    SELECT u.user_id,
        mp.promotion_date,
        COUNT(DISTINCT u.id) as num_jobs_switched,
        MIN(start_date) as career_started_date
    FROM playground.user_experiences u
    INNER JOIN manager_promo mp 
        on u.user_id=mp.user_id
    WHERE u.start_date<mp.promotion_date
    GROUP BY u.user_id, mp.promotion_date
) tt
GROUP BY num_jobs_switched
ORDER BY 2 DESC

4. Analysis Considerations

• The solution doesn’t account for data scientists who never become managers
• Creating time-based buckets could provide more digestible insights
• Consider normalizing for years of experience

• Begin by understanding the business problem thoroughly
• Make clear assumptions about the data and metrics
• Develop testable hypotheses before writing queries
• Create step-by-step solutions that demonstrate your analytical process
• Acknowledge limitations in your approach

Mastering SQL case studies requires both technical SQL skills and business acumen to translate hypothetical scenarios into actionable insights.