8 Match Group SQL Interview Questions (Updated 2024)
At Match Group, SQL crucial for analyzing user interaction data to improve the myraid of dating apps in their portfolio. That's why Match Group almost always evaluates jobseekers on SQL coding questions during interviews for Data Science and Data Engineering positions.
To help you prepare for the Match Group SQL interview, here’s 8 Match Group SQL interview questions – can you solve them?
8 Match Group SQL Interview Questions
SQL Question 1: Analyze user matching behaviour
As a data analyst at Match Group, you have an access to "matches" and "messages" tables. The "matches" table records which users have connected on the dating platform, indicating mutual interest. The "messages" table records which matches have actually exchanged messages.
Your task is to write a SQL query that can identify the percentage of matches that have exchanged at least one message within a particular time period.
Here are some data samples for better illustration:
Example Input:
| match_id | user_id_1 | user_id_2 | match_date |
|---|---|---|---|
| 001 | 123 | 456 | 2022-01-01 |
| 002 | 265 | 789 | 2022-01-15 |
| 003 | 999 | 111 | 2022-02-07 |
| 004 | 456 | 123 | 2022-03-01 |
| 005 | 234 | 567 | 2022-03-20 |
Example Input:
| message_id | sender_id | receiver_id | message_date |
|---|---|---|---|
| 1001 | 123 | 456 | 2022-01-03 |
| 1002 | 999 | 111 | 2022-02-11 |
| 1003 | 265 | 789 | 2022-02-01 |
| 1004 | 789 | 265 | 2022-02-03 |
| 1005 | 456 | 123 | 2022-03-05 |
Answer:
The above SQL query firstly identifies which matches have exchanged at least one message after the match date using a subquery and creates a CTE of matches that actually sent messages. Then it groups matches by month and counts total number of matches and number of matches that exchanged messages. Finally, it calculates the percentage of matches that exchanged messages for each month.
For more window function practice, try this Uber SQL Interview Question within DataLemur's online SQL code editor:
SQL Question 2: User Matching and Engagement
Imagine that you are a data engineer at Match Group, the company that operates a number of dating services. You are tasked with designing a database schema to keep track of users, their preferences, messages, and matches. The business wants you to design a system that can answer questions like:
- How many users sent messages in the last 30 days?
- How many new matches occurred in the past 30 days?
Two tables would be needed for this schema: and . Users will contain user information and preferences. Matches will contain match information and messages.
Example Input:
| user_id | user_name | preference | last_login |
|---|---|---|---|
| 1 | JohnD | female | 2021-06-08 00:00:00 |
| 2 | JaneD | male | 2021-05-18 00:00:00 |
| 3 | SamP | female | 2021-06-20 00:00:00 |
| 4 | SarahL | male | 2021-07-10 00:00:00 |
Example Input:
| match_id | user1_id | user2_id | match_date | message_date | message_content |
|---|---|---|---|---|---|
| 100 | 1 | 2 | 2021-06-08 00:00:00 | 2021-06-09 00:00:00 | Hi, nice to meet you! |
| 200 | 3 | 4 | 2021-06-20 00:00:00 | 2021-07-01 00:00:00 | Hey, how's it going? |
| 300 | 2 | 4 | 2021-06-18 00:00:00 | 2021-07-11 00:00:00 | Hello, how are you? |
Answer:
PostgreSQL Queries to solve the above queries are given below:
In the first query, we are counting the number of unique from the table where the is within the last 30 days.
The second query counts the number of rows from the table where the is within the last 30 days. This gives us the number of new matches.
Note: These queries assume that is the sender of the message. For the case where either user in a match could send a message, UNION or another method would be needed to ensure all users who sent a message are counted.
SQL Question 3: Can you describe a cross-join and its purpose?
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
Let's say you were building a Machine Learning model that attempts to score the probability of a customer purchasing a Match Group product. Before working in Pandas and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Match Group products.
Here's a cross-join query you could run:
Cross-joins are useful for generating all possible combinations, but they can also create huge tables if you're not careful. For instance, if you had 10,000 potential customers and Match Group had 500 different product SKUs, the resulting cross-join would have 5 million rows!
SQL Question 4: Analyzing Click-Through-Rate for Match Group
Given two tables, and , where table tracks click events on Match Group's marketing ads, and table records the registrations made, calculate the Click-Through-Rate (CTR) of the ads. The 'CTR' is defined as the number of users who have registered after clicking on the ad, divided by the total number of clicks, for each day.
Example Input:
| click_id | user_id | click_date | ad_id |
|---|---|---|---|
| 1145 | 823 | 06/01/2022 | 345 |
| 4587 | 732 | 06/01/2022 | 567 |
| 9652 | 498 | 06/02/2022 | 345 |
| 2341 | 125 | 06/03/2022 | 743 |
| 3485 | 879 | 06/03/2022 | 567 |
Example Input:
| registration_id | user_id | registration_date | ad_id |
|---|---|---|---|
| 6751 | 823 | 06/02/2022 | 345 |
| 1919 | 732 | 06/02/2022 | 567 |
| 3845 | 498 | 06/03/2022 | 345 |
| 5602 | 125 | 06/04/2022 | 743 |
| 2962 | 879 | 06/05/2022 | 567 |
Answer:
This SQL query first joins the table with the table on and , and ensure the registration date is after the click date. Then it groups by click date and calculates the CTR as the count of distinct users who registered after clicking, divided by the count of distinct users who clicked. The CTR is then converted into percentage and rounded to two decimal places.
To solve a similar problem about calculating rates, try this SQL interview question from TikTok within DataLemur's interactive coding environment:
SQL Question 5: In the context of database schema design, can you give an example of a one-to-one relationship vs. a one-to-many relationship?
In database schema design, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a US citizen and their social-security number (SSN) - each citizen has one SSN, and each SSN belongs to one person.
On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. A teacher's relationship with their classes is an example of this - a teacher can teach many classes, but each class is only associated with one teacher.
SQL Question 6: Average Usage Time Per User
As an analyst for Match Group, your task is to understand how long users spend on their platform. The data you have is stored in two tables. One table, 'users', stores information on each user. The other table, 'sessions', logs every user session and its duration.
Write an SQL query that calculates the average session duration for each user, ordered by the user id in ascending order.
Example Input:
| user_id | user_name | signup_date |
|---|---|---|
| 100 | Molly | 2022-04-01 |
| 200 | James | 2022-02-15 |
| 300 | Lee | 2022-03-20 |
Example Input:
| session_id | user_id | session_start | session_end |
|---|---|---|---|
| 1500 | 100 | 2022-04-02 10:00:00 | 2022-04-02 11:30:00 |
| 1600 | 200 | 2022-02-16 12:00:00 | 2022-02-16 14:00:00 |
| 1700 | 300 | 2022-03-21 09:00:00 | 2022-03-21 10:30:00 |
| 1800 | 100 | 2022-04-03 14:00:00 | 2022-04-03 15:45:00 |
| 1900 | 300 | 2022-03-22 16:00:00 | 2022-03-22 17:00:00 |
Example Output:
| user_id | avg_session_mins |
|---|---|
| 100 | 97.50 |
| 200 | 120.00 |
| 300 | 90.00 |
Answer:
This reports the average number of minutes spent on the platform per session for each individual user. The part calculates the length of each user's session in minutes. These are then averaged over each user using the function, resulting in a table of average session durations per user.
SQL Question 7: Can you explain the concept of a constraint in SQL?
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
Say you were storing sales analytyics data from Match Group's CRM inside a database. Here's some example constraints you could use:
-
PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.
-
FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.
-
NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.
-
UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.
-
CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.
-
DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"
SQL Question 8: Calculate the Age of Users and Their Average Subscription Price
In Match Group's database, users' date of birth is stored along with their subscription information. Suppose we want to find out the average subscription price paid by each age group. The age group is determined by finding the age based on the user's date of birth and current date then rounding down to the nearest decade (20s, 30s, 40s etc.). Use the ABS(), ROUND(), and arithmetic operators in your SQL query.
Assume current date as '2022-06-30' for this exercise.
Example Input:
| user_id | username | dob |
|---|---|---|
| 1 | user_abc | 1995-01-18 |
| 2 | user_def | 1982-12-30 |
| 3 | user_ghi | 1977-06-15 |
| 4 | user_jkl | 1990-07-20 |
| 5 | user_mno | 1975-02-22 |
Example Input:
| sub_id | user_id | start_date | end_date | price |
|---|---|---|---|---|
| 101 | 1 | 2022-01-01 | 2023-01-01 | 9.99 |
| 102 | 2 | 2022-02-01 | 2023-02-01 | 19.99 |
| 103 | 3 | 2022-03-01 | 2023-03-01 | 29.99 |
| 104 | 4 | 2022-04-01 | 2023-04-01 | 9.99 |
| 105 | 5 | 2022-05-01 | 2023-05-01 | 29.99 |
Answer:
This query first calculates the age by subtracting the date of birth from the current date, retrieves the year part, and then rounds down to the nearest decade. After that, it joins this result with the table and averages the subscription price for each age group. The result is sorted by the age_group.
Preparing For The Match Group SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Match Group SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the above Match Group SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like FAANG tech companies and tech startups.
Each problem on DataLemur has hints to guide you, step-by-step solutions and best of all, there is an online SQL coding environment so you can instantly run your SQL query and have it executed.
To prep for the Match Group SQL interview you can also be helpful to practice SQL problems from other tech companies like:
In case your SQL coding skills are weak, forget about going right into solving questions – go learn SQL with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as LEAD window function and handling NULLs in SQL – both of these come up routinely during SQL interviews at Match Group.
Match Group Data Science Interview Tips
What Do Match Group Data Science Interviews Cover?
In addition to SQL interview questions, the other topics covered in the Match Group Data Science Interview are:
- Statistics and Probability Questions
- Python or R Programming Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Resume-Based Behavioral Questions
How To Prepare for Match Group Data Science Interviews?
To prepare for Match Group Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG tech companies
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