Top 18 Google SQL Interview Questions (Updated for 2025)

SQL interview questions are pretty common in data-related roles, including for Google. SQL developer positions are expected to surge by 21% over the next three years, generating more than 284,000 new jobs across data science, analytics, and other specialized data fields in companies spanning multiple industries.

In line with the demands, SQL questions pop up in Google data science interviews frequently. However, FAANGs have elevated their candidate expectations. They’ve moved on from asking “What is a JOIN?” to discussing how you might apply your SQL knowledge to resolve business operations. Nonetheless, interviewers will likely still ask candidates to write SQL queries around, say, Google custom data.

Today, we’re looking specifically at the SQL portion of Google interviews. We cover what you need to know about Google SQL interview questions, how they align with various Google roles, and SQL practice problems to help you prep.

Google’s interview process is rigorous and constantly evolving, mirroring the growing complexity of data-centric roles and rapid technological advancements. Each stage evaluates your technical expertise, problem-solving acumen, and cultural fit.

While SQL questions have traditionally been reserved for the technical and on-site interviews, recent 2025 patterns suggest a shift toward real-world application and more advanced SQL topics. This is what to expect:

Initial Phone Screen

In 2025, Google’s recruiters are moving beyond traditional resume walkthroughs. This stage may now include basic SQL concept checks, including distinguishing various JOIN types or comparing GROUP BY with HAVING clauses, to ensure you have the essential groundwork.

Additionally, expect to discuss your familiarity with modern data infrastructure, particularly tools like BigQuery and other cloud-based database solutions.

Google’s Interview Warmup tool, part of the “Grow with Google” initiative, allows candidates to practice answering behavioral and technical questions using AI before the interview.

Technical Screen

The technical screen now includes real-time data processing challenges and exercises involving semi-structured data. You would need to apply SQL in dynamic and often ambiguous contexts.

There’s a pronounced emphasis on optimization as well. Interviewers may ask you to identify and resolve performance bottlenecks or refactor queries for scalability across large datasets.

Moreover, expect questions that explore advanced SQL concepts such as window functions, Common Table Expressions (CTEs), and nuanced handling of NULL values. Furthermore, candidates are increasingly asked to explain how SQL integrates with other tools, including Python for data transformation and Tableau or Looker for visualization, highlighting Google’s preference for versatile, cross-functional analysts.

On-Site Interview

The on-site round features case study-based interviews, where you’ll be expected to mine insights from datasets and offer strategic recommendations, such as optimizing Google Ads performance or diagnosing traffic drops in YouTube analytics.

A strong focus on cloud computing is evident, with questions involving Google Cloud Platform (GCP) services and managing distributed data systems.

The final round of interviews includes intermediate to advanced SQL questions using CASE, JOIN, sub-queries, and complex queries. Complementing the technical components, behavioral questions now delve into your past experiences managing data-related challenges.

If you need additional help, be sure to check out the SQL module in our Data Science Course. It offers a solid review of basic-to-advanced concepts. You might also want to see our Top 25+ Data Science SQL Interview Questions.

Like all FAANG companies, Google relies heavily on data. SQL is a go-to tool for processing and analyzing that data. Google SQL interview questions don’t just ask the basics. These interviews tend to ask case study SQL questions. In other words, you’ll be presented with more practical problems and real data and be asked to write queries for that dataset. The most common questions to get asked include:

• Basic Google SQL interview questions - These are typically definition-based questions that come up on the technical screen. One tip: Develop your ability to explain these basic SQL concepts in layman’s terms.
• Intermediate SQL query questions - These types of questions test your knowledge in writing queries and statements in SQL. You’ll be presented with a dataset and asked to write SQL code to return a specific value.
• Advanced SQL questions - Finally, you may be asked SQL scenario-based interview questions that will require you to write advanced queries. With this type of question, you’ll be asked to write queries that address a specific case and use a range of SQL clauses, from basics like SELECT and FROM to advanced ones like HAVING.

1. Explain the different types of joins in SQL.

A JOIN is a clause in SQL that’s used to join rows from two or more tables based on a common column between the tables. It is used for merging tables and retrieving data. The most common types of joins include:

• Inner Join: An INNER JOIN returns all rows from two or more tables when the JOIN condition is met.
• Left Join: It returns rows from the left table when a matching row from the right meets the JOIN condition.
• Right Join: Similar to a left join, but rows are returned from the right table when the JOIN condition on the left is met.
• Full Join: Full joins return all rows from the left and right when a match exists in any of the tables.

2. What is the PRIMARY KEY in SQL?

Primary keys are constraints that uniquely identify each record. One thing to note: Primary keys cannot have NULL values; all values must be UNIQUE. A table can have only one primary key, but the primary key can consist of single or multiple columns.

3. What are constraints?

Constraints in SQL are rules that can be applied to the data type in a table. They are used to limit the type of data that can be stored in a particular column within a table. Some common types used in SQL are:

• NOT NULL - This constraint prevents null values from being stored in a column.
• UNIQUE - This constraint says that values in a column must be unique. The primary key uses the UNIQUE constraint.
• PRIMARY KEY - This constraint is used to specify which field is the primary key.
• FOREIGN KEY - A foreign key is a constraint that can uniquely identify a row in another table.

4. What’s the difference between DELETE and TRUNCATE statements in SQL?

DELETE is used to remove specific data from a table. This statement is a DML command, and it’s slower than TRUNCATE. One key difference: You can roll back data after using DELETE. TRUNCATE, on the other hand, is a DDL command that is used to delete all the rows from a table.

5. Write a query to return the number of songs played on each date for each user

We have a table called song_plays that tracks each time a user plays a song. Write a query to return the number of songs played on each date for each user. Note: If a user plays the same song twice during the day, the count should be two.

Approach: Group the data by user and date, then use COUNT(*) to calculate the total number of song plays per user per day. This method ensures that every individual play is counted, even if the same song is played multiple times.

6. What is query optimization?

Inefficient SQL queries can drain a database, leading to slow performance and loss of service. Optimization is especially critical when working with production databases. As such, query optimization is the process of making SQL queries more efficient. More efficient queries provide outputs faster and minimize the impact on the database.

7. We’re given two tables: a user’s table with demographic information and their neighborhood and a neighborhood table. Write a query that returns all neighborhoods with 0 users.

Perform a LEFT JOIN from the neighborhoods data to the users data using the neighborhood ID as the joining key. This keeps all neighborhoods in the result, even if there’s no matching user. Then filter for rows where the user ID (or any non-nullable user column) is NULL—these represent neighborhoods with zero associated users. This method ensures that you’re capturing all neighborhoods without any residents in the users’ dataset.

8. Given the tables below, select the top three departments with at least ten employees and rank them according to the percentage of their employees making over $100,000 in salary.

Approach:

1. Join the employees and departments tables using the department_id.
2. Group by department and count total employees and those with salary > 100000.
3. Filter departments that have at least 10 employees using a HAVING clause.
4. Calculate the percentage of high-earning employees per department.
5. Order by this percentage in descending order and limit the results to the top 3.
6. Use RANK() or ROW_NUMBER() to rank the departments if needed.

9. Given a user table, write a query to get the cumulative number of new users added by day, with the total reset every month.

This question first seems like it could be solved by running a COUNT(*) and grouping by date. Or maybe it’s just a regular cumulative distribution function? But we must notice that we are grouping by a specific month and date interval. And when the next month comes around, we want to reset the count of the number of users.

10. Given a table of product subscriptions with a subscription start date and end date for each user, write a query that returns true or false whether or not each user has a subscription date range that overlaps with any other user.

Perform a self-join on the subscriptions table where the user_id is not equal, and the date ranges overlap by checking if one user’s start_date is less than the other’s end_date and their end_date is greater than the other’s start_date; then, use an aggregate or EXISTS condition to return a boolean flag for each user indicating whether any such overlap exists.

11. Given a table of students and their SAT test scores, write a query to return the two students with the closest test scores with the score difference.

Use a self-join on the scores table to compare each student’s score with every other student’s, calculate the absolute difference between their scores, then order by score difference and alphabetical order of names to break ties, finally limiting the result to the top one pair with the smallest difference.

12. We’re given two tables: a user’s table with demographic information and their neighborhood and a neighborhood table. Write a query that returns all neighborhoods with 0 users.

Hint: Our predicament is to find all the neighborhoods without users. In a sense, we need all the neighborhoods that do not have a single user living in them. This means we have to introduce the concept of the existence of a column in one table but not in the other.

Approach:

Perform a LEFT JOIN from the neighborhoods table to the users table using the neighborhood ID, then filter for rows where the user ID is NULL, which indicates neighborhoods that have no associated users, and select the neighborhood names from those rows.

13. Given a table of transactions and products, write a query to return the product ID, product price, and average transaction price of all products with a price greater than the average transaction price.

Approach: First, calculate the average transaction price by joining the transactions and products tables and computing price * quantity for each transaction, then take the average of those values; next, compare each product’s individual price from the products table to this average, and return only those products whose price is greater than the computed average.

14. Let’s say we have two tables, transactions and products. Hypothetically, the transactions table consists of over a billion rows of purchases bought by users.

Hint: To solve this, we need to break it down into several steps. First, we should find a way to select all the instances in which a user purchased 2 or more products simultaneously. How can we use user_id and created_at to accomplish this?

Approach: Start by self-joining the transactions table on user_id and created_at to find pairs of products purchased by the same user at the same time. Then, filter out duplicate or reversed pairs by ensuring one product ID is alphabetically before the other. Group by the product pairs and count the frequency of each pair; finally, join with the products table to get the names and return the top five most frequent pairs.

15. Write an SQL query using the database schema to find the percentage of customers who order drinks with their meal.

Design a database for a stand-alone fast food restaurant.

Based on the above database schema, write an SQL query to find the top three items with the highest revenue sold yesterday.

Note: We will only record customers who sign up for our rewards program or order by delivery. Customers will be recorded in the orders and customers tables only if they match one of those criteria. Deliveries will be recorded in the orders and deliveries tables only if they were delivered.

Approach:

1. Percentage of Customers Ordering Drinks with Meals: Join the orders, order_items, and items tables to identify orders that include both drink and meal items. Then, divide that count by the total number of valid customer orders (from rewards or delivery) to compute the percentage.
2. Top 3 Items by Revenue (Yesterday): Filter the order_items by order date (yesterday), calculate revenue per item (price * quantity), group by item, sum the revenues, and select the top 3 items using ORDER BY total_revenue DESC LIMIT 3.

16. Display the top three users by downloads each day. Order your data by date and then by daily_rank.

Let’s say you work at a file-hosting website. You have information on users’ daily downloads in the download_facts table

Use the window function RANK to display the top three users by downloads each day. Order your data by date and then by daily_rank

Input:

download_facts table

Column	Type
user_id	INTEGER
date	DATE
downloads	INTEGER

Output:

Column	Type
daily_rank	INTEGER
user_id	INTEGER
date	DATE
downloads	INTEGER

Approach: Use a window function RANK() partitioned by date and ordered by downloads DESC to assign ranks to users for each day. Then, filter the result to only include rows where daily_rank <= 3, and order the output by date and daily_rank for final presentation.

17. Write a query to return the top 3 highest-paid employees per department. If a department has fewer than 3 employees, return the top 2 or 1. Include full employee name, department name, and salary. Sort by department name (asc) and salary (desc).

Approach:

1. Join the employees and departments tables using department_id.
2. Concatenate first_name and last_name to create the full employee_name.
3. Use a window function like RANK() or DENSE_RANK() partitioned by department and ordered by salary DESC to rank employees within each department.
4. Filter to include only ranks 1 to 3.
5. Order the final result by department_name ASC, then salary DESC.

18. Design a fast-food restaurant database.

1. Write a query to find the top 3 highest revenue items sold yesterday.
2. Write a query to find the percentage of customers who ordered drinks with their meal.

Example Answer by our candidate:

Tables

users

user_id (PK)	created_date	user_type
1	2020-05-25	walkin

orders

id	user_id	item_id	qty	created_date
1	1	234	1	2020-09-09
2	1	432	2	2020-09-09

items

id (PK)	description	price
234	chicken burger	10.28
432	bread sticks	3.50

---

1. Top Three Highest Revenue Items Sold Yesterday

Revenue = item price × quantity

SELECT
    o.item_id,
    SUM(o.qty * i.price) AS item_revenue
FROM orders o
INNER JOIN items i ON i.id = o.item_id
WHERE o.created_date = CURRENT_DATE - INTERVAL '1 DAY'
GROUP BY o.item_id
ORDER BY item_revenue DESC
LIMIT 3;

2. Percentage of Customers Who Ordered Drinks With Their Meal

Calculate the number of users who ordered both a drink and a non-drink item, divided by the total unique users who ordered.

WITH drinks AS (
    SELECT id
    FROM items
    WHERE description ILIKE '%drink%'
),
non_drinks AS (
    SELECT id
    FROM items
    WHERE id NOT IN (SELECT id FROM drinks)
),
user_agg AS (
    SELECT
        user_id,
        MAX(CASE WHEN d.id IS NOT NULL THEN 1 ELSE 0 END) AS drinks_flag,
        MAX(CASE WHEN nd.id IS NOT NULL THEN 1 ELSE 0 END) AS non_drinks_flag
    FROM orders o
    LEFT JOIN drinks d ON o.item_id = d.id
    LEFT JOIN non_drinks nd ON o.item_id = nd.id
    GROUP BY user_id
)
SELECT
    100.0 * COUNT(*) / (SELECT COUNT(DISTINCT user_id) FROM orders) AS percentage_users_with_drinks
FROM user_agg
WHERE drinks_flag = 1 AND non_drinks_flag = 1;

Google dramatically revamped its interview process in 2025, reflecting advancements in automation and introducing more complexity in data-related role interviews. These roles are increasingly intertwined with AI and machine learning (ML) technologies.

Here’s an overview of the roles where SQL interview questions are common, along with their responsibilities and how AI/ML integration affects their work:

Business Intelligence Analyst

Traditionally employed to use SQL to generate insights, maintain reports, and run analyses, business intelligence analysts at Google are now expected to also integrate SQL outputs with AI tools for predictive analytics, enhancing forecasting and trend analysis capabilities.

Business Intelligence Engineer

BI engineers work at the intersection of product, data, and business strategy. They use data to drive business decisions that improve customer experience. BI engineers often use AI-driven tools to automate data pipelines and optimize business intelligence systems at Google.

Data Analyst

Google data analysts perform a range of key tasks. Google analysts tend to derive business insights from data and provide that information to key stakeholders. These roles vary by the team you work with; for example, a data analyst on the Google Ads team will have a much different role than one working on Google Drive.

Analysts must be proficient in SQL to extract insights from large datasets. Interviews may include scenario-based SQL problems.

Data Scientist

At Google, data scientists perform a variety of roles, and they’re tested on many different tools and skills. Although SQL questions are asked, these interviews tend to focus more on statistics, algorithms, and machine learning. But be prepared for basic SQL syntax questions and solvable queries.

Data scientists heavily rely on AI and ML to build predictive models, integrate with SQL for data preparation, and leverage tools like TensorFlow or PyTorch for deep learning tasks.

AI/ML Engineer

These roles involve developing and deploying AI models across Google’s products. While SQL is not the primary focus, understanding how to integrate AI outputs with SQL databases is crucial.

Data Engineer

Data engineers with an AI focus are responsible for building scalable data pipelines that feed AI models. They must ensure that SQL databases are optimized for real-time data processing and integration with AI tools.

If you need additional help, be sure to check out the SQL module in our Data Science Course. It offers a solid review of basic-to-advanced concepts. You might also want to see our Top 25+ Data Science SQL Interview Questions. Feel free to also explore our AI Interviewer to refine your Google SQL interview strategy.