SQL for Data Science Interviews: Practice Questions, Patterns, and Solutions

Let's be clear: the SQL interview for a data scientist is not a suggestion. It's a gate. Whether it's for a Data Analyst, Product Analyst, or Data Scientist role at a company like Meta, Google, or Amazon, you will face a technical screen designed to test your data manipulation fluency.

But here’s the secret: they aren't trying to trick you with obscure syntax. They are testing your ability to recognize and solve a handful of critical business problems using SQL patterns.

This guide is your playbook. We will break down the exact patterns that appear in over 80% of data science SQL practice questions. Master these, and you won't just pass the technical screen sql questions; you'll ace them.

Pattern 1: Mastering SQL Window Functions

If you master one area of advanced SQL interview questions, make it window functions. They allow you to perform calculations across a set of table rows that are somehow related to the current row.

What they're really testing: Can you perform row-level comparisons without resorting to slow, complex self-joins?

Classic Interview Question:

You have a table logins with user_id and login_time. Find the time difference between each user's current and previous login.

Solution: This is a classic use case for LAG(). We partition the data by user_id and order it by login_time to ensure we're looking at the correct previous login for each user.

-- Solution using Google BigQuery/PostgreSQL Syntax
WITH UserLoginLag AS (
    SELECT
        user_id,
        login_time,
        LAG(login_time, 1) OVER (PARTITION BY user_id ORDER BY login_time) AS previous_login_time
    FROM
        logins
)
SELECT
    user_id,
    login_time,
    previous_login_time,
    TIMESTAMP_DIFF(login_time, previous_login_time, MINUTE) AS minutes_since_last_login
FROM
    UserLoginLag
WHERE
    previous_login_time IS NOT NULL;

More Window Function Questions:

• What is the difference between RANK() and DENSE_RANK()? (Hint: DENSE_RANK doesn't leave gaps after ties).
• Find the second-highest salary by department. (Hint: Use a CTE with DENSE_RANK()).

Pattern 2: The Top-N-Per-Group Problem

This is a fan favorite in Amazon data scientist sql questions. They want to see if you can find the top N records within a specific category.

What they're really testing: Your understanding of how window functions and CTEs work together to solve complex filtering problems.

Classic Interview Question:

You have a table of products and sales. Find the top 3 best-selling products within each product category.

Solution: The key is to first rank the products within each category using ROW_NUMBER() or RANK(), and then filter on that rank in an outer query. You cannot use a window function in a WHERE clause directly.

-- Solution using a Common Table Expression (CTE)
WITH RankedProducts AS (
    SELECT
        product_id,
        product_category,
        total_sales,
        ROW_NUMBER() OVER(PARTITION BY product_category ORDER BY total_sales DESC) as rn
    FROM
        product_sales
)
SELECT
    product_id,
    product_category,
    total_sales
FROM
    RankedProducts
WHERE
    rn <= 3;

Pattern 3: SQL Query for Cohort Retention Analysis

Calculating user churn rate with SQL is a cornerstone of product analytics. A cohort is a group of users who share a common characteristic, most often their sign-up month. Retention analysis tracks how many users from a cohort return over time.

What they're really testing: Can you join a table to itself and perform date arithmetic to build a business-critical report? This is a staple of the sql for product analyst interview.

Classic Interview Question:

Using a users table with user_id and creation_date and an events table with user_id and event_date, calculate monthly user retention.

Solution: This multi-step process involves identifying a user's cohort month, then calculating the "month number" of each subsequent activity, and finally pivoting the data.

-- Simplified solution to find Month 1 retention
WITH UserCohorts AS (
    SELECT
        user_id,
        DATE_TRUNC(creation_date, MONTH) AS cohort_month
    FROM
        users
),
ActivityByMonth AS (
    SELECT
        user_id,
        DATE_TRUNC(event_date, MONTH) AS activity_month
    FROM
        events
    GROUP BY 1, 2
)
SELECT
    c.cohort_month,
    COUNT(DISTINCT c.user_id) AS total_users,
    COUNT(DISTINCT CASE
        WHEN a.activity_month = c.cohort_month + INTERVAL '1 month' THEN c.user_id
    END) AS retained_month_1
FROM
    UserCohorts c
LEFT JOIN
    ActivityByMonth a ON c.user_id = a.user_id
GROUP BY 1
ORDER BY 1;

Pattern 4: Solving Gaps and Islands Problems in SQL

The SQL gaps and islands problem is considered an advanced challenge. It involves finding consecutive sequences (islands) and breaks in those sequences (gaps).

What they're really testing: Your logical thinking and ability to create groups from sequential data using row numbering tricks.

Classic Interview Question:

You have a table of user activity with user_id and activity_date. Find the longest consecutive daily streak of activity for each user.

Solution: The trick is to subtract a ROW_NUMBER() sequence from the date. For consecutive dates, this difference will be constant, creating a grouping key for each "island" of activity.

WITH DateGroups AS (
    SELECT
        user_id,
        activity_date,
        DATE_SUB(activity_date, INTERVAL ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY activity_date) DAY) as date_group
    FROM
        user_activity
)
SELECT
    user_id,
    MAX(streak) as longest_streak
FROM (
    SELECT
        user_id,
        date_group,
        COUNT(*) as streak
    FROM
        DateGroups
    GROUP BY
        user_id, date_group
) AS Streaks
GROUP BY 1
ORDER BY 2 DESC;

Pattern 5: Cumulative Sums & Running Totals

This pattern is fundamental for creating reports that show growth over time.

What they're really testing: Your knowledge of the SUM() window function with an ORDER BY clause to create a running total.

Classic Interview Question:

Given a table of daily transactions with transaction_date and amount, calculate the cumulative (running) total revenue over time.

Solution: This is a surprisingly simple and powerful use of window functions.

SELECT
    transaction_date,
    SUM(amount) AS daily_revenue,
    SUM(SUM(amount)) OVER (ORDER BY transaction_date) AS cumulative_revenue
FROM
    transactions
GROUP BY
    transaction_date
ORDER BY
    transaction_date;

Pattern 6: Pivoting Data with CASE WHEN

Before PIVOT clauses were common, this was the only way to turn rows into columns. It's still a required skill as it's more flexible and works in any SQL dialect.

What they're really testing: Can you reshape data for reporting using conditional aggregation?

Classic Interview Question:

You have a sales table with month, category, and revenue. Transform this data so that each row is a month, and there are separate columns for the revenue of 'Electronics', 'Clothing', and 'Home Goods'.

Solution:

SELECT
    month,
    SUM(CASE WHEN category = 'Electronics' THEN revenue ELSE 0 END) AS electronics_revenue,
    SUM(CASE WHEN category = 'Clothing' THEN revenue ELSE 0 END) AS clothing_revenue,
    SUM(CASE WHEN category = 'Home Goods' THEN revenue ELSE 0 END) AS home_goods_revenue
FROM
    sales
GROUP BY
    month
ORDER BY
    month;

You've Seen the Patterns. Now Master Them.

This guide covers the core patterns you'll face in a sql coding challenge for data science. But knowing them is one thing; executing them flawlessly under pressure is another. To do that, you need practice. Lots of it.

Frequently Asked Questions (FAQ)

Q: What is the best way to practice for the SQL interview?
A: The best way is active recall. Don't just read solutions. Read a problem, close the page, and try to write the query from scratch in a text editor or on a whiteboard. Then, compare your solution to the correct one. Repeat this for all the major patterns.

Q: How fast do I need to be?
A: Speed is less important than accuracy and communication. Interviewers want to see your thought process. Talk through your approach before you start writing code. Explain the joins you'll use, the CTEs you'll build, and why. A correct, well-explained solution that takes 15 minutes is better than a buggy, silent one that takes 5.

Q: What if I don't know the answer to a question?
A: It happens. Don't panic. Start by explaining what you do know. "I'm not sure of the exact syntax here, but my approach would be to first create a CTE that ranks users by their purchase date. Then, from that CTE, I would filter for a rank of 1. Can I try to write that out?" This shows problem-solving skills even if you forget a specific function name.

Q: What's the difference between SQL questions for a data analyst vs. a data scientist?
A: There's a huge overlap. Generally, sql interview questions for a data analyst might focus more on reporting and dashboarding (e.g., cohort analysis, pivoting). Questions for data scientists might lean more towards data wrangling for modeling (e.g., finding last non-null values, feature engineering) and A/B test analysis, but you should be prepared for both.