At Bread Financial, SQL is used for extracting and analyzing financial data to identify high-risk transactions and predict customer behaviors. It is also used for managing databases to ensure secure storage of sensitive customer information and efficient processing of payment transactions, the reason Bread Financial asks SQL questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
Thus, to help you ace the Bread Financial SQL interview, we've curated 9 Bread Financial SQL interview questions in this blog.
In Bread Financial, there are several accounts where loan transactions take place regularly. Write a SQL query to analyze the average amount of loan transactions per month for specific users using a dataset with the following fields:
transaction_id | user_id | transaction_date | loan_amount |
---|---|---|---|
4517 | 123 | 01/15/2022 00:00:00 | 50000 |
4203 | 265 | 01/26/2022 00:00:00 | 20000 |
3485 | 123 | 01/30/2022 00:00:00 | 30000 |
7039 | 362 | 02/18/2022 00:00:00 | 10000 |
5160 | 123 | 02/25/2022 00:00:00 | 60000 |
We want to analyze this to gain insights into user loan habits for potential special promotions or interventions.
The output should be aggregated monthly, and look like the following:
mth | user | avg_loan_amount |
---|---|---|
1 | 123 | 40000 |
1 | 265 | 20000 |
2 | 123 | 60000 |
2 | 362 | 10000 |
This PostgreSQL query uses the function to get the month from the transaction date. Then, it calculates the average loan amount for each user per month with the function. The clause is used to group results by month and user. It further uses the clause to order the results by month and average loan amount in descending order. This enables us to see which users, on average, have the highest loan amounts each month.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
Given a table of Bread Financial employee salary data, write a SQL query to find all employees who make more than their own boss.
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 |
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 problem directly within the browser on DataLemur:
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 solution above is tough, you can find a step-by-step solution here: Employees Earning More Than Their Boss.
A full outer join returns all rows from both tables, including any unmatched rows, whereas an inner join only returns rows that match the join condition between the two tables.
For a tangible example, suppose you had a table of Bread Financial orders and Bread Financial customers.
Here's a SQL inner join using the orders and customers tables:
This query will return rows from the orders and customers tables that have matching values. Only rows with matching values will be included in the results.
Here is an example of a using the orders and customers tables:
This query will return all rows from both the orders and customers tables, including any rows that do not have matching values. Any rows with null values for either table will be included in the results.
Bread Financial is interested in knowing the details of its customers who have at least one checking account, no loan account and whose credit card limit is above $5,000. For this purpose, they want to filter the records from the customer database.
Here are the ‘accounts’ and ‘credit_cards’ example database that Bread Financial hold:
account_id | user_id | account_type |
---|---|---|
10001 | 123 | checking |
10002 | 265 | savings |
10003 | 362 | checking |
10004 | 451 | loan |
10005 | 625 | checking |
card_id | user_id | card_type | card_limit |
---|---|---|---|
2001 | 123 | Visa | 5000 |
2002 | 265 | MasterCard | 8000 |
2003 | 362 | Visa | 4000 |
2004 | 451 | MasterCard | 7000 |
2005 | 625 | Visa | 5500 |
We need to extract the for those customers who meet the given conditions.
Here is the SQL query that can fulfill this condition in PostgreSQL:
This query first joins the two tables on , and then applies the filter conditions using WHERE, AND and NOT IN commands. We check for customers with a checking account and excluding those with a loan account, with a credit card limit of more than $5000. Using DISTINCT, we ensure that we don't have any repeated in the result.
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. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
In Bread Financial, they offer loans to customers for various financial needs. As a data analyst, you are asked to find the average loan amount disbursed per month.
You have access to the table organized as below:
loan_id | user_id | disbursal_date | loan_amount |
---|---|---|---|
3245 | 123 | 06/08/2022 | 3000 |
5632 | 265 | 06/10/2022 | 5000 |
9823 | 552 | 06/18/2022 | 2500 |
7189 | 523 | 07/26/2022 | 4000 |
2317 | 987 | 07/05/2022 | 6000 |
You need to create a table which gives you the average loan amount disbursed per month.
month | avg_loan_amount |
---|---|
6 | 3500 |
7 | 5000 |
Here's a PostgreSQL query to solve it:
This PostgreSQL query uses the function to get the month from the disbursal_date column. It groups the data by month and then uses the AVG function to find the average loan_amount for each month. Finally, the results are ordered by month.
To practice a very similar question try this interactive Facebook Active User Retention Question which is similar for aggregation over time or this Amazon Average Review Ratings Question which is similar for < calculating averages monthly.
The keyword removes duplicates from a query.
Suppose you had a table of Bread Financial customers, and wanted to figure out which cities the customers lived in, but didn't want duplicate results.
name | city |
---|---|
Akash | SF |
Brittany | NYC |
Carlos | NYC |
Diego | Seattle |
Eva | SF |
Faye | Seattle |
You could write a query like this to filter out the repeated cities:
Your result would be:
city |
---|
SF |
NYC |
Seattle |
Bread Financial provides loans to its customers. You are a data analyst who is looking into loans data. Your task is to find the total loan amount disbursed for each loan status (for example: Approved, Rejected, Under Review, etc.).
Assume there is a table, , which contains information about loans.
loan_id | user_id | issue_date | loan_status | loan_amount |
---|---|---|---|---|
8253 | 123 | 06/08/2022 | Approved | 5000 |
8391 | 265 | 06/10/2022 | Rejected | 10000 |
6938 | 362 | 06/18/2022 | Under Review | 7500 |
7130 | 192 | 07/26/2022 | Approved | 3500 |
8582 | 981 | 07/05/2022 | Rejected | 4000 |
For example, based on the table above, the sum of 'Approved' loans is 14000, and there is $7500 'Under Review'.
loan_status | total_loan_amount |
---|---|
Approved | 8500 |
Rejected | 14000 |
Under Review | 7500 |
The query for this problem would be,
This query first groups the data in by and then calculates the sum of for each group. This sum is then returned alongside the in the result.
At Bread Financial, the marketing department would like to launch an email campaign specifically targeting customers who use Gmail for personal banking. Your task is to write an SQL query that retrieves all records of customers where the email field ends with "@gmail.com".
customer_id | first_name | last_name | account_type | |
---|---|---|---|---|
129 | John | Doe | john.doe@gmail.com | Personal |
201 | Sally | Smith | sally.smith@yahoo.com | Personal |
528 | Jason | Taylor | jason.taylor@hotmail.com | Business |
602 | Rebecca | Mills | rebecca.mills@gmail.com | Personal |
847 | Zoe | Martin | zoe.martin@gmail.com | Business |
931 | Alex | Brown | alex.brown@yahoo.com | Business |
This SQL query uses the keyword to filter down the table by the column. The in the clause is a wildcard character that matches any sequence of characters. So will match any string that ends with "@gmail.com". This will return all customer records where the email ends with "@gmail.com".
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 Bread Financial SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each interview question has hints to guide you, step-by-step solutions and most importantly, there is an online SQL coding environment so you can easily right in the browser your query and have it executed.
To prep for the Bread Financial SQL interview it is also wise to solve SQL questions from other financial services companies like:
Explore the latest happenings at Bread Financial and see how they're making waves in the financial industry!
But if your SQL coding skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this free SQL tutorial.
This tutorial covers SQL topics like CTE vs. Subquery and filtering strings based on patterns – both of these pop up often during Bread Financial SQL assessments.
Besides SQL interview questions, the other question categories tested in the Bread Financial Data Science Interview are:
To prepare for the Bread Financial Data Science interview have a strong understanding of the company's values and mission – this will be key to acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got: