8 Stripe SQL Interview Questions (Updated 2024)
Stripe employees use SQL for analyzing transactional data, such as payment processing, revenue trends, and customer behavior, as well as managing databases for secure and efficient information retrieval in the fintech sphere, including storing and retrieving sensitive customer information and payment data. That is why Stripe often asks SQL questions in interviews for Data Analyst, Data Science, and BI jobs.
So, to help you practice for the Stripe SQL interview, here's 8 Stripe SQL interview questions in this article.
8 Stripe SQL Interview Questions
SQL Question 1: Repeated Payments
Sometimes, payment transactions are repeated by accident; it could be due to user error, API failure or a retry error that causes a credit card to be charged twice.
Using the transactions table, identify any payments made at the same merchant with the same credit card for the same amount within 10 minutes of each other. Count such repeated payments.
Table:
| Column Name | Type |
|---|---|
| transaction_id | integer |
| merchant_id | integer |
| credit_card_id | integer |
| amount | integer |
| transaction_timestamp | datetime |
Example Input:
| transaction_id | merchant_id | credit_card_id | amount | transaction_timestamp |
|---|---|---|---|---|
| 1 | 101 | 1 | 100 | 09/25/2022 12:00:00 |
| 2 | 101 | 1 | 100 | 09/25/2022 12:08:00 |
| 3 | 101 | 1 | 100 | 09/25/2022 12:28:00 |
| 4 | 102 | 2 | 300 | 09/25/2022 12:00:00 |
| 6 | 102 | 2 | 400 | 09/25/2022 14:00:00 |
Example Output:
| payment_count |
|---|
| 1 |
Answer:
The SQL query for the problem is as follows:
To practice this question question on DataLemur's free online SQL code editor, solve this Stripe SQL Interview Question:
SQL Question 2: Employees Earning More Than Managers
Given a table of Stripe employee salaries, write a SQL query to find employees who make more than their own boss.
Stripe Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Solve this interview question interactively on DataLemur:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the code above is confusing, you can find a step-by-step solution with hints here: Highly-Paid Employees.
SQL Question 3: What does the constraint do, and when might you use it?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail.
For example, say you had a database that stores ad campaign data from Stripe's Google Analytics account.
Here's what some constraints could look like:
In this example, the constraint is used to ensure that the "budget" and "cost_per_click" fields have positive values. This helps to ensure that the data in the database is valid and makes sense in the context of ad campaigns.
You can also use the constraint to ensure that data meets other specific conditions. For example, you could use a constraint to ensure that the "start_date" is before the "end_date" for each ad campaign.
SQL Question 4: Track Transactions for Stripe Customers
Imagine that Stripe would like to analyze their transactions data to see which customer is making the highest transactions within a single month.
Given the below two tables, and , write a PostgreSQL query to return the customer_id and total amount of the customer who had the highest total transaction amount for each month. The results should be sorted by year, month and total amount in descending order.
Note: Consider that the is in format .
Example Input:
| customer_id | customer_name | sign_up_date |
|---|---|---|
| 1 | John | 2021-05-10 |
| 2 | Anna | 2021-04-22 |
| 3 | Bob | 2021-07-16 |
| 4 | Cindy | 2021-06-30 |
| 5 | Evan | 2021-08-01 |
Example Input:
| transaction_id | customer_id | transaction_date | amount |
|---|---|---|---|
| 101 | 1 | 2022-01-15 | 100.00 |
| 102 | 3 | 2022-01-20 | 150.00 |
| 103 | 1 | 2022-01-21 | 200.00 |
| 104 | 4 | 2022-02-15 | 250.00 |
| 105 | 2 | 2022-02-20 | 150.00 |
| 106 | 5 | 2022-03-01 | 300.00 |
| 107 | 1 | 2022-03-15 | 350.00 |
| 108 | 2 | 2022-03-18 | 100.00 |
Answer:
Here is a sample PostgreSQL query to get the result.
This query first calculates the sum of the transaction amounts for each client for each month by using a common table expression. It then joins this result with the Customers table to get the corresponding customer_name. The WHERE condition then filters out the customers with the highest transaction amount for each month. The results are then sorted by year, month, and total_amount in descending order.
SQL Question 5: What is database denormalization?
Database denormalization is when you add redundancy to a database, and break typical normalization rules (codified by the 1st, 2nd, 3rd normal forms).
Denormalization is typically used to improve the performance of a database, particularly when the database is being for OLAP (Online Analytical Processing) purposes. By denormalizing a database, you can reduce the number of joins that are required to retrieve data, which can greatly improve the speed of queries since joins are costly and slow. However, denormalization can also introduce some problems, such as increased data redundancy and the need for more complex update and delete operations since data has been duplicated into multiple tables.
In general, denormalization should be used with caution and only after careful consideration of the trade-offs involved. It's typically better to start with a well-normalized database design then denormalize only if your database isn't scaling as well as you want.
SQL Question 6: Average Transaction Amount Per Customer
You are a data analyst at Stripe. Your manager wants to understand the customers' spending habits. For analysis, she asked you to find the average transaction amount for each customer for the year 2021. Write a SQL query to solve this problem.
Here is sample data of how the transactions table might look like:
Example Input:
| transaction_id | customer_id | transaction_date | transaction_amount |
|---|---|---|---|
| 1254 | 789 | 01/15/2021 | $500.00 |
| 2365 | 456 | 02/08/2021 | $320.00 |
| 7845 | 123 | 03/27/2021 | $100.00 |
| 3265 | 789 | 04/11/2021 | $250.00 |
| 9871 | 123 | 05/02/2021 | $150.00 |
We want to generate a result that has the average transaction amount for each customer:
Example Output:
| customer_id | avg_transaction_amount |
|---|---|
| 789 | $375.00 |
| 456 | $320.00 |
| 123 | $125.00 |
Answer:
This PostgreSQL query calculates the average transaction amount for each customer for the transactions made in the year 2021. function is used to extract year from and then the function is used to compute the average. is used here to calculate this average for each customer.
To practice a very similar question try this interactive Stripe Repeated Payments Question which is similar for customer transaction analysis or this Amazon Average Review Ratings Question which is similar for average calculation per user.
SQL Question 7: What's the difference between a one-to-one vs. a one-to-many relationship between two entities? Give examples.
In database schema design, a one-to-one relationship between two entities is where each entity is associated with only one instance of the other entity. For example, the relationship between a car and a license plate is one-to-one, because each car can only have one licensce plate, and each licensce plate belongs to exactly one car.
On the other hand, a one-to-many relationship is where one entity can be associated with multiple instances of the 2nd entity. For example, a teacher can teach multiple classes, but each class is associated with only one teacher.
SQL Question 8: Calculate Click-Through Conversion Rate for Stripe's Digital Services
Stripe is a technology company that builds economic infrastructure for the internet. Let's say, for instance, they want to analyze customer behavior on their product portfolio, by calculating the click-through conversion rate from a page view of digital services to add a service to the cart. For this, we need two tables, a table that logs each view of a digital service, and a table that logs when a service is added to the cart.
Example Input:
| view_id | user_id | view_date | service_id |
|---|---|---|---|
| 101 | 001 | 2022-06-08 | 2001 |
| 102 | 007 | 2022-06-10 | 2002 |
| 103 | 003 | 2022-06-18 | 2002 |
| 104 | 003 | 2022-06-23 | 2001 |
| 105 | 009 | 2022-06-26 | 2003 |
Example Input:
| add_id | user_id | add_date | service_id |
|---|---|---|---|
| 501 | 003 | 2022-06-18 | 2002 |
| 502 | 007 | 2022-06-12 | 2002 |
| 503 | 003 | 2022-06-25 | 2001 |
| 504 | 010 | 2022-06-27 | 2003 |
| 505 | 008 | 2022-06-29 | 2002 |
Answer:
The SQL query for our solution would look something like this:
This query first calculates the total count of page views and cart adds for each service separately, then joins these two counts together by service ID, and finally computes the conversion rate as the number of adds to cart divided by the number of views for each service. Here, function is used to avoid division by zero errors. The final output of the query would be the conversion rate for each service listed on Stripe's platform.
To practice a similar problem on DataLemur's free online SQL code editor, attempt this SQL interview question asked by Facebook:
Preparing For The Stripe SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Stripe SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the above Stripe SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Microsoft, Google, and Meta.
Each DataLemur SQL question has multiple hints, full answers and most importantly, there's an online SQL code editor so you can easily right in the browser your SQL query answer and have it executed.
To prep for the Stripe SQL interview you can also be useful to solve SQL problems from other payment & fintech companies like:
Discover the latest developments and announcements from Stripe, committed to making online commerce easier and more efficient!
In case your SQL foundations are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this free SQL tutorial.
This tutorial covers topics including joins and rank window functions – both of which come up frequently in Stripe SQL assessments.
Stripe Data Science Interview Tips
What Do Stripe Data Science Interviews Cover?
In addition to SQL interview questions, the other topics tested in the Stripe Data Science Interview are:
- Probability & Statistics Questions
- Python or R Coding Questions
- Product & Business Analytics Questions
- ML Modelling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Stripe Data Science Interviews?
To prepare for the Stripe Data Science interview have a deep understanding of the company's values and mission – this will be clutch for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG tech companies
- A Refresher on Product Analytics, SQL & ML
- Amazing Reviews (1000+ reviews, 4.5-star rating)
Read other Data Science use cases from FinTech companies like Stripe to understand how SQL is used in the industry!