8 SoFi SQL Interview Questions (Updated 2024)
At financial giant SoFi, SQL does the heavy lifting for extracting and analyzing lending data for risk management. For this reason SoFi almost always asks SQL problems in interviews for Data Science, Data Engineering and Data Analytics jobs.
So, to help you study for the SoFi SQL interview, this blog covers 8 SoFi SQL interview questions – able to solve them?
8 SoFi SQL Interview Questions
SQL Question 1: Find the VIP Users at SoFi
SoFi (Social Finance Inc.) is an American online personal finance company that provides student loan refinancing, mortgages, and personal loans. Suppose you work as a data analyst in SoFi. Your manager wants you to identify the VIP users. A user is considered a VIP if they have taken more than 3 loans each with amount greater than $20,000 in the past year. Write a SQL query to find these VIP users.
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 123 | John | Doe |
| 265 | Sarah | Brown |
| 362 | Alex | Smith |
| 192 | Peter | Johnson |
| 981 | Linda | Davis |
Example Input:
| loan_id | customer_id | amount | loan_date |
|---|---|---|---|
| 2171 | 123 | 25,000 | 06/08/2020 |
| 7802 | 123 | 23,000 | 07/08/2021 |
| 5293 | 265 | 18,000 | 06/18/2021 |
| 6352 | 192 | 27,000 | 07/26/2020 |
| 4517 | 123 | 21,500 | 07/05/2020 |
Answer:
This SQL query first selects the customers from the table who have taken out more than 3 loans each greater than $20,000 in the past year. This is achieved by the clause together with and . Then it joins this derived table with the table on to get the first and last names of these VIP users.
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: Retrieve Monthly Average Loan Ratings
Given a dataset of customer reviews for SoFi's different loan products, write a SQL query to return the monthly average ratings for each product.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-08-06 | 50001 | 4 |
| 7802 | 265 | 2022-10-06 | 69852 | 4 |
| 5293 | 362 | 2022-18-06 | 50001 | 3 |
| 6352 | 192 | 2022-26-07 | 69852 | 3 |
| 4517 | 981 | 2022-05-07 | 69852 | 2 |
Answer:
In PostgreSQL, you can use the function to truncate the date to the nearest month, and the window function over a partitioned window to achieve this as follows:
This query calculates the monthly average rating () for each product (). It divides the data into partitions by month and product, computes the average over each partition, and orders the final result set by month and product.
Note that the output will be a list of all reviews, with an additional column for the monthly average. In order to reduce this to one line per month and product, you will need to group by month and product:
The resulting table should match the Example Output:
| mth | product | avg_stars |
|---|---|---|
| 2022-06-01 | 50001 | 3.50 |
| 2022-06-01 | 69852 | 4.00 |
| 2022-07-01 | 69852 | 2.50 |
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 3: Can you describe the difference between a clustered and a non-clustered index?
Clustered and non-clustered indexes are both used to improve query performance, but they differ in how they are implemented.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not. As a result, a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query efficiency & performance, a clustered index is generally faster for SELECT queries (reads) but updates (writes) to a clustered index are slower, as they require the data rows to be physically rearranged.
SQL Question 4: Filter Customers by Loan Status and Income
Suppose you are asked to pull a list of all SoFi's customers that have taken a personal loan that is either "past due" or "charged off", and have an annual salary above $50,000. Additionally, you are also required to show only customers that are from California.
Example Input:
| customer_id | state | annual_income |
|---|---|---|
| 3001 | California | 85000 |
| 3002 | Nevada | 75000 |
| 3003 | California | 48000 |
| 3004 | Florida | 60000 |
| 3005 | California | 70000 |
Example Input:
| loan_id | customer_id | loan_status |
|---|---|---|
| 4001 | 3001 | Paid |
| 4002 | 3002 | Past Due |
| 4003 | 3001 | Past Due |
| 4004 | 3005 | Charged Off |
| 4005 | 3003 | Charged Off |
Example Output:
| customer_id | state | annual_income | loan_status |
|---|---|---|---|
| 3001 | California | 85000 | Past Due |
| 3005 | California | 70000 | Charged Off |
Answer:
Here is a PostgreSQL query that you could use to answer this question:
This query performs an inner join of the and tables on . It then filters the resulting table for entries where the customer is in California, earns above $50,000 annually, and have a loan status of either 'Past Due' or 'Charged Off'. Hence, providing us with the desired customers' info.
SQL Question 5: What does and do?
Both the and window functions are used to access a row at a specific offset from the current row.
However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.
Say you had a table of salary data for SoFi employees:
| name | salary |
|---|---|
| Amanda | 130000 |
| Brandon | 90000 |
| Carlita | 80000 |
You could use the function to output the salary of each employee, along with the next highest-paid employee:
This would yield the following output:
| name | salary | next_salary |
|---|---|---|
| Amanda | 130000 | 90000 |
| Brandon | 90000 | 80000 |
| Carlita | 80000 | NULL |
Swapping for would get you the salary of the person who made just more than you:
| name | salary | next_salary |
|---|---|---|
| Amanda | 130000 | NULL |
| Brandon | 90000 | 130000 |
| Carlita | 80000 | 90000 |
SQL Question 6: Calculate the average loan amount
SoFi, or Social Finance, is a company offering a range of lending and wealth management services. Let's simulate a scenario where you are required to find the average loan amount taken by members from SoFi.
We have a table which includes details of each loan taken by members, and the information contains the member's user_id, loan_id, loan_amount and loan_taken_date.
Example Input:
| loan_id | user_id | loan_taken_date | loan_amount |
|---|---|---|---|
| 101 | 321 | 01/01/2022 | 15000 |
| 102 | 564 | 01/02/2022 | 20000 |
| 103 | 783 | 01/03/2022 | 25000 |
| 104 | 321 | 01/04/2022 | 20000 |
| 105 | 564 | 01/05/2022 | 30000 |
We want to find the average loan amount taken by members, with results rounded to 2 decimal places.
Expected Output:
| average_loan_amount |
|---|
| 22000.00 |
Answer:
The PostgreSQL SQL query to generate this output would be as follows:
This query calculates the average value of the loan_amount column from the loans table using the AVG function. The ROUND function is used to round the average loan amount to 2 decimal places.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for calculating average value or this Amazon Average Review Ratings Question which is similar for calculating averages over groups.
SQL Question 7: What is a primary key?
A primary key is a column or set of columns in a table that uniquely identifies each row in the table. The primary key is used to enforce the uniqueness and non-nullability of the rows in the table.
In a SQL database, a primary key is defined using the constraint. For example, say you had a table of :
In this example, the column is the primary key of the SoFi employees table. It is defined as an integer and is marked as the primary key using the constraint.
A table can have only one primary key, but the primary key can consist of multiple columns. For example, say you had a table of SoFi customer transactions:
In the above example, the primary key of the Orders table consists of two columns: TransactionID and ProductID. This means that the combination of OrderID and ProductID must be unique for every row in the table.
SQL Question 8: Finding Average Loan Amount by Loan Type
SoFi (Social Finance Inc.) is a company that provides a variety of lending products, such as student loan refinancing, personal loans, and home loans. The data team wants to understand the average loan amount for each type of loan for the last year. This will help the team understand the demand for each type of loan product and align its strategies accordingly.
The loans data table has the following columns: , , , and (mm/dd/yyyy). Write an SQL query to find the average loan amount for each type of loan issued in the last year.
Example Input:
| loan_id | user_id | loan_type | loan_amount | loan_issued_date |
|---|---|---|---|---|
| 1001 | 101 | Student Loan | 25000 | 08/10/2021 |
| 1002 | 102 | Home Loan | 500000 | 06/20/2021 |
| 1003 | 103 | Personal Loan | 10000 | 01/15/2022 |
| 1004 | 104 | Student Loan | 22000 | 04/07/2022 |
| 1005 | 105 | Home Loan | 450000 | 05/09/2021 |
Answer:
This query filters the table for loans issued within the date range of the last year. It then groups the remaining loans by and calculates the average loan amount for each type.
Example Output:
| loan_type | avg_loan_amt |
|---|---|
| Student Loan | 23500 |
| Home Loan | 475000 |
This result interprets that the average Student Loan issued last year was 475,000. This insight can be helpful for SoFi in aligning their lending strategies and tailoring their marketing for different loan offerings.
SoFi SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the SoFi SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the earlier SoFi SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each interview question has multiple hints, detailed solutions and crucially, there's an interactive coding environment so you can right in the browser run your query and have it checked.
To prep for the SoFi SQL interview you can also be wise to solve interview questions from other tech companies like:
However, if your SQL query skills are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL topics like aggregate window functions and removing NULLs – both of these come up routinely in SQL interviews at SoFi.
SoFi Data Science Interview Tips
What Do SoFi Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems to prepare for the SoFi Data Science Interview are:
- Statistics and Probability Questions
- Python or R Programming Questions
- Analytics and Product-Metrics Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for SoFi Data Science Interviews?
To prepare for SoFi Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Facebook, Google, & Amazon
- a refresher covering SQL, AB Testing & ML
- over 900+ reviews on Amazon & 4.5-star rating
You should also look into SoFi's interview resources portal.