10 Genius Sports SQL Interview Questions (Updated 2024)

Updated on

June 23, 2024

Genius Sports employees write SQL queries often for analyzing vast sports data for statistical insights and developing robust data management systems for real-time sports data. Because of this, Genius Sports almost always evaluates jobseekers on SQL query questions during interviews for Data Analyst, Data Science, and BI jobs.

Thus, to help you practice, we've curated 10 Genius Sports SQL interview questions – can you solve them?

10 Genius Sports SQL Interview Questions

SQL Question 1: Identify the Whale Users in "Genius Sports"

"Genius Sports" is a sports data and technology company. For them, a power user or VIP user might be an individual that places frequent and large sports bets.

For our purposes, let's consider the below two scenarios to identify a power user or whale user:

  1. Users who bet more than 5000 units of currency in a single bet.
  2. Users who have placed more than 100 bets in the last month.

You have two tables, and . The table records every individual bet placed at Genius Sports and the table contains user information.

Example Input:
Example Input:


In this proposed solution, first, we create a common table expression that lists the user ids, the total number of bets they placed in the last month and their maximum single bet amount.

Then, we extract the users who fall into our whale user condition to another common table expression .

Finally, we join with the table to get their usernames. This PostgreSQL query returns the user_id and usernames of the whale users according to the conditions mentioned above.

To practice another SQL customer analytics question where you can code right in the browser and have your SQL solution automatically checked, try this Walmart Labs SQL Interview Question: Walmart Labs SQL Interview Question

SQL Question 2: Top 3 Department Salaries

Imagine you had a table of Genius Sports employee salary data. Write a SQL query to find the top three highest paid employees in each department.

Genius Sports Example Input:

1Emma Thompson38001
2Daniel Rodriguez22301
3Olivia Smith20001
4Noah Johnson68002
5Sophia Martinez17501
8William Davis68002
10James Anderson40001

Example Input:

1Data Analytics
2Data Science

Example Output:

Data AnalyticsJames Anderson4000
Data AnalyticsEmma Thompson3800
Data AnalyticsDaniel Rodriguez2230
Data ScienceNoah Johnson6800
Data ScienceWilliam Davis6800

Solve this question and run your code right in DataLemur's online SQL environment:

Top 3 Department Salaries


We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.

If the code above is tough, you can find a detailed solution with hints here: Top 3 Department Salaries.

SQL Question 3: What's the main difference between the 'BETWEEN' and 'IN' operators in SQL?

is used to select rows that match a range of values, whereas the operator checks for values in a specified list of values.

For example, say you were a Data Analyst at Genius Sports and had a table of advertising campaign data.

To find campaigns with between 500and500 and 10k in spend, you could use BETWEEN`:

To find ad campaigns that were run on Facebook and Google's Display Network, you could use :

Genius Sports SQL Interview Questions

SQL Question 4: Calculate the Total Views per League per Day

Genius Sports is a sports data and technology company. Imagine you have been given access to a table which contains all the website views for various sports leagues across the world. The task is to write a SQL query to calculate the total number of views each league gets per day. You are expected to use SQL window function to solve this task. The dataset also captures the day the views were recorded.

Given the table, create a new table that includes each unique , , and the total number of per league per day.

Example Input:


Example Output:



This query uses the window function to calculate the total views per per . It first partitions the data by and , and then aggregates the number of views in each partition, giving the total views per day for each league. It finally orders the result by and .

p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur

DataLemur SQL Questions

SQL Question 5: Could you explain what a self-join is and provide examples of when it might be used?

A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the FROM clause, and give each instance of the table a different alias. You can then join the two instances of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

Self-joins are the go-to technique for any data analysis that involves pairs of the same thing, like identifying pairs of products that are frequently purchased together like in this Walmart SQL interview question.

For another example, say you were doing an HR analytics project and needed to analyze how much all Genius Sports employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of Genius Sports employees who work in the same department:

This query returns all pairs of Genius Sports employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Genius Sports employee being paired with themselves).

SQL Question 6: Player Performance Analysis

Genius Sports is a sports data and technology company that provides data management and integrity services to major sports leagues and organizations. Imagine you're a data analyst at Genius Sports tasked with designing a database to track the performance of football players. This database should include tables for Teams, Players, and Matches. Teams play Matches, and Players belong to Teams.

The tables might look as follows:

1Blue Eagles
2Red Warriors
3Yellow Sparrows
1John Doe1
2Jane Smith2
3Robert Brown3

An interesting question to ask here is, "How many matches has each team won? List all teams and their win counts in descending order (i.e., the team with the most wins first)."


You can solve this question using a SQL operation and the clause.

This query will return a list of all teams and their win counts in descending order. The operation combines rows from and where the team ID matches the winning team ID. The clause groups the result by team name. The function counts the number of rows in each group (i.e., the number of wins for each team), and the clause sorts the result in descending order by win count.

SQL Question 7: How do the and window functions differ from each other?

As the name implies, the window function ranks each row within your window/partition. If two rows have the same rank, the next number in the ranking will be the previous rank plus the number of duplicates. For example, if you've got three records tied for 5th place, they'll get the values 5, 6, and 7, and the next row after this 3-way tie will have a rank of 8.

The function assigns a distinct rank to each row within a partition based on the provided column value with no gaps. This function will assign the same rank to two rows if they have the same rank, and give the next row the next rank number. To make this more concrete, imagine you had three records at rank 5 – then, the next rank would be 6.

SQL Question 8: Filter Player Data based on Specific Conditions

Given a table of player performance data, write a SQL query to filter out the records for players who have an average score above 50 in Football, has played more than 10 games with a win_rate of above 0.5 and have been playing for more than 3 years.

Example Input:
Example Output:


This SQL query selects all the columns from the table where the sport is , the average score is higher than 50, the number of games played is more than 10, the winning rate is higher than 0.5, and the player has been playing for more than 3 years. This results in a filtered table that lists the records of players who meet all the specified conditions.

SQL Question 9: Calculate the Click Through Rates for Advertisements

As an analyst at Genius Sports, you have been tasked with evaluating the efficiency of digital advertising. Specifically, the marketing department is interested in the click-through rates (CTR) of different advertisements on various sporting events.

CTR is calculated as the number of clicks an ad receives divided by the number of times the ad is shown (impressions), expressed as a percentage.

Your task is to calculate the CTR for each advertisement, sorted by the advertisement ID in ascending order.

Example Input:


Example Input:

4001Football Match
2500Basketball Game
7600Golf Tournament
8900Tennis Match
9100Hockey Game

Example Output:

1Football Match25.00%
2Basketball Game25.00%
3Golf Tournament18.75%
4Tennis Match13.33%
5Hockey Game20.00%


The SQL query above joins the and tables using as the joining field. It then calculates the CTR by diving the number of clicks by the number of impressions, and multiplies by 100 to get the result in percentage. The result is rounded to two decimal places for better presentation. The result is then ordered by in ascending order.

To practice a similar SQL problem on DataLemur's free interactive coding environment, attempt this Facebook SQL Interview question: Facebook Click-through-rate SQL Question

SQL Question 10: Can you describe the difference between a clustered and a non-clustered index?

Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.

The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.

Preparing For The Genius Sports SQL Interview

The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the above Genius Sports SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies. DataLemur SQL and Data Science Interview Questions

Each interview question has multiple hints, fully explained answers along with a discussion board to see how others solved it and best of all, there's an interactive coding environment so you can instantly run your SQL query and have it graded.

To prep for the Genius Sports SQL interview it is also useful to solve SQL problems from other tech companies like:

However, if your SQL coding skills are weak, don't worry about diving straight into solving questions – go learn SQL with this SQL tutorial for Data Scientists & Analysts.

DataLemur SQL Course

This tutorial covers SQL concepts such as INTERCEPT/EXCEPT and window functions – both of which come up routinely in SQL job interviews at Genius Sports.

Genius Sports Data Science Interview Tips

What Do Genius Sports Data Science Interviews Cover?

In addition to SQL query questions, the other types of questions tested in the Genius Sports Data Science Interview include:

Genius Sports Data Scientist

How To Prepare for Genius Sports Data Science Interviews?

I'm a bit biased, but I believe the best way to prep for Genius Sports Data Science interviews is to read the book Ace the Data Science Interview.

The book solves 201 interview questions taken from tech companies like Google & Microsoft. It also has a crash course covering Stats, ML, & Data Case Studies. And finally it's helped thousands of people land their dream job in data, which is why it's got over 1000+ 5-star reviews on Amazon.

Ace the Data Science Interview