9 Entain SQL Interview Questions (Updated 2025)

At Entain, SQL is often used for analyzing gaming behaviors to optimize user experience, and managing vast sports and betting databases. Unsurprisingly this is why Entain LOVES to ask SQL query questions in interviews for Data Science, Analytics, and & Data Engineering jobs.

So, to help you ace the Entain SQL interview, here’s 9 Entain SQL interview questions – can you solve them?

9 Entain SQL Interview Questions

SQL Question 1: Identify the Top PC Gamers at Entain

Entain is a multinational sports betting and gaming group, owning lots of brands under its umbrella. You have been given a database containing information on users' gaming activities. For the purpose of promoting a new line of PC games, you have been tasked to identify power users in this category of games. These are users who play PC games frequently and spend a significant amount of money.

gaming_activity Example Input:

Answer:

SELECT 
    user_id, 
    COUNT(DISTINCT game_id) AS total_games_played,
    SUM(amount_spent) AS total_spent 
FROM 
    gaming_activity 
WHERE 
    game_category = 'PC Games'
GROUP BY 
    user_id 
ORDER BY 
    total_spent DESC, total_games_played DESC 
LIMIT 
    10;

The SQL query provided will identify the top 10 users (or less if there aren't 10 users) who have spent the most on PC games within the "gaming_activity" table. The users will be ordered in descending order of the total amount spent, and in case of a tie, the user who has played the most games will be ranked higher. Each row of the result will contain the user's ID, the total number of distinct PC games the user has played, and the total amount the user has spent on PC games.

To practice a super-customer analysis question on DataLemur's free online SQL coding environment, try this Microsoft Azure Cloud SQL Interview Question:

SQL Question 2: Analyzing Betting Data with SQL Window Function

Entain is a multinational sports betting and gaming group. As a data analyst for Entain, your task is to analyze a dataset of users' bets and find the average betting amount per user for each week. Also, find the total bet amount for the preceding week for comparison. If there is no preceding week data (i.e., for the first week), just return the present week's total bet amount.

You have the following tables:

bets Example Input:

Example Output:

Answer:

WITH weekly_bets AS (
  SELECT
    user_id,
    DATE_TRUNC('week', bet_date) AS week_start_date,
    AVG(bet_amount) OVER (PARTITION BY user_id, DATE_TRUNC('week', bet_date)) AS avg_bet_amount,
    SUM(bet_amount) OVER (PARTITION BY user_id, DATE_TRUNC('week', bet_date)) AS week_total_bet_amount
  FROM
    bets
)
SELECT
  week_start_date,
  user_id,
  avg_bet_amount,
  COALESCE(LAG(week_total_bet_amount) OVER (PARTITION BY user_id ORDER BY week_start_date), avg_bet_amount) AS prev_week_total_bet_amount
FROM
  weekly_bets
ORDER BY
  week_start_date, user_id;

The sample solution uses the DATE_TRUNC function to create week-based bins of data and calculates the average betting amount for each user for each week. The LAG function is used to get the total betting amount from the preceding week. If no preceding week is available (i.e., for the first week), COALESCE ensures that the present week's total betting amount is returned instead. The PARTITION BY logic is used to calculate these values separately for each user.

To solve a similar window function question on DataLemur's free interactive SQL code editor, solve this Google SQL Interview Question:

SQL Question 3: What do stored procedures do, and when would you use one?

Stored procedures in SQL are like recipes in a cookbook. Just like a recipe tells you the ingredients and instructions for making a particular dish, a stored procedure tells the DBMS the logic/statements needed to perform a specific task. Just like you can use a recipe to make the same dish over and over again, you can use a stored procedure to repeat the same task multiple times with different input parameters (which is why stored procedures are so damn useful!).

Say you were a Data Analyst working on a HR analytics project. A common sub-task you might have to do is calculate the average salary for a given department at Entain, which would be perfect for a stored procedure:

CREATE FUNCTION get_average_salary(department_name TEXT)
RETURNS NUMERIC AS
$BODY$
BEGIN
  RETURN (SELECT AVG(salary) FROM entain_employees WHERE department = department_name);
END;
$BODY$
LANGUAGE 'plpgsql';

To call this stored procedure and find the average salary for the Data Science department you'd execute a query like this:

SELECT get_average_salary('Data Science');

SQL Question 4: Calculate Average Bet Amount and Total Wins For Active Users

As a Data Analyst for Entain, you're interested in understanding more about the betting behavior of active users on the platform. You've been given two tables, user_bets and user_wins. The user_bets table contains the bet amounts for each user, and the user_wins table contains the win amounts for each user. Your task is to write a PostgreSQL query to calculate the average bet amount and total wins for each active user.

Here's an example of how to format your tables and provide your answer:

user_bets Example Input:

user_wins Example Input:

Example Output:

Answer:

SELECT b.user_id, 
       AVG(b.bet_amount) as average_bet, 
       COALESCE(SUM(w.win_amount), 0) as total_wins
FROM user_bets b
LEFT JOIN user_wins w
ON b.user_id = w.user_id
WHERE b.active = 'Yes'
GROUP BY b.user_id;

In this query, we use a combination of LEFT JOIN, AVG(), SUM() functions, and GROUP BY and WHERE clauses. We first join the user_bets and user_wins tables on the user_id column. We then filter for active users and group the results by user_id. This provides each active user's betting and winning details. We calculate the average bet amount and total wins for each active user. If a user has no wins, we return a total win sum of 0 using the COALESCE function.

SQL Question 5: Can you describe the meaning of a constraint in SQL in layman's terms?

Think of SQL constraints like the rules of a game. Just like a game needs rules to keep things fair and fun, a database needs constraints to keep things organized and accurate.

There are several types of SQL constraints like:

NOT NULL: This constraint is like a bouncer at a nightclub - it won't let anything NULL through the door. UNIQUE: This constraint is like a VIP list - only special, one-of-a-kind values get in. PRIMARY KEY: This constraint is like an elected official - it's made up of NOT NULL and UNIQUE values and helps identify each row in the table. FOREIGN KEY: This constraint is like a diplomatic ambassador - it helps establish relationships between tables. CHECK: This constraint is like a referee - it makes sure everything follows the rules. DEFAULT: This constraint is like a backup plan - it provides a default value if no other value is specified.

So, whether you're playing a game or organizing a database, constraints are an important part of the process!

SQL Question 6: Retrieving customer records starting with specific letters

You are required to filter down Entain's customer records database to find and list out all customer records where the first name starts with 'A' and the last name starts with 'B'.

customers Example Input:

Answer:

SELECT * 
FROM customers
WHERE first_name LIKE 'A%' AND last_name LIKE 'B%'

This query will find all records in the 'customers' table where the first name starts with 'A' and the last name starts with 'B'. The '%' character in the LIKE condition is used to define wildcards (missing letters) both before and after the pattern. In this case, we only want to define the letters at the start of the first and last names, hence the '%' character is after the letters 'A' and 'B'. The query will return the customer records for Alfred Brown, Alan Black, Andrew Blaze, and Antony Bellamy.

SQL Question 7: What is the purpose of the SQL constraint UNIQUE?

{#Question-7}

A UNIQUE constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.

For example, if you had Entain sales leads data stored in a database, here's some constraints you'd use:

CREATE TABLE entain_leads (
    lead_id INTEGER PRIMARY KEY,
    first_name VARCHAR(255) NOT NULL,
    last_name VARCHAR(255) NOT NULL,
    company VARCHAR(255) NOT NULL,
    email VARCHAR(255) NOT NULL UNIQUE,
    phone VARCHAR(255) NOT NULL UNIQUE
);

In this example, the UNIQUE constraint is applied to the "email" and "phone" fields to ensure that each Entain lead has a unique email address and phone number. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two leads had the same email address or phone number.

SQL Question 8: Calculate Average Betting Amount for Each Betting Type and Round to Nearest Whole Number

Entain is an online betting and gambling company. You are given a bets table where each row represents a bet made by a user. Each bet has an unique bet_id, the user_id of the user who made the bet, the betting_type (such as 'sports', 'casino', 'poker', etc.), bet_amount which is the amount of money placed on the bet, and bet_date which is the date on which the bet was made.

Your task is to write a SQL query to calculate, for each betting type, the average betting amount rounded to the nearest whole number.

bets Example Input:

Answer:

    SELECT betting_type, ROUND(AVG(bet_amount)) as avg_bet_amount
    FROM bets
    GROUP BY betting_type;

This query first groups all bet entries based on their betting type. Then it calculates the average bet amount for each group using the AVG() function. Finally, it rounds the average bet amount for each group to the nearest whole number using the ROUND() function.

Example Output:

Here, for instance, the 'sports' betting type had bet amounts of 500 and 400, so the average amount (450) is shown. Similarly, the calculations were performed for 'casino' and 'poker' betting types.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for performing group calculations or this Snapchat Sending vs. Opening Snaps Question which is similar for dealing with aggregations and rounding.

SQL Question 9: Calculate the Daily Betting Volume

Entain is a multinational sports betting and gaming group. Each day, thousands of users place a variety of bets on different sports games. In this question, assume that you're provided with a 'bets' table that includes a timestamp for each bet, the user_id of the user who placed the bet, and the amount of the bet. Your task is to write a SQL query to calculate the total amount bet on each day.

bets Example Input:

Example Output:

Answer:

Provided below is the SQL query that gives us the total amount of bet placed each day.

SELECT
  DATE(bet_timestamp) AS day,
  SUM(amount) AS total_amount
FROM
  bets
GROUP BY
  DAY
ORDER BY
  DAY;

In the SQL query:

• The DATE function is used to extract the date from the 'bet_timestamp' column. This strips off the time part and retains only the date.
• The SUM function is used to calculate the total bet amount for each date.
• The GROUP BY clause groups the data by the 'day' column, which represents the date on which the bets were placed.
• The ORDER BY clause sorts the resulting rows by the 'day' column.
• The data will show the daily betting volume with each day and the total amount bet on that day.

Preparing For The Entain SQL Interview

The best way to prepare for a Entain SQL interview is to practice, practice, practice. In addition to solving the above Entain SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.

Each DataLemur SQL question has hints to guide you, detailed solutions and most importantly, there is an online SQL code editor so you can right in the browser run your query and have it executed.

To prep for the Entain SQL interview you can also be a great idea to solve SQL problems from other tech companies like:

• Activation Blizzard SQL questions

• Microsoft SQL questions

• Sony SQL questions

• EA SQL questions

In case your SQL coding skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers topics including grouping by multiple columns and filtering strings using LIKE – both of these show up routinely in Entain interviews.

Entain Data Science Interview Tips

What Do Entain Data Science Interviews Cover?

In addition to SQL query questions, the other types of problems to prepare for the Entain Data Science Interview are:

• Stats Interview Questions
• Coding Questions in Python or R
• Product Analytics Questions
• ML Interview Questions
• Behavioral & Resume-Based Questions

How To Prepare for Entain Data Science Interviews?

To prepare for Entain Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions sourced from Microsoft, Amazon & startups
• a refresher on Stats, SQL & ML
• over 900+ 5-star reviews on Amazon