Rithm Capital employees use SQL often for analyzing trading patterns and managing portfolio data. That's why Rithm Capital covers SQL questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
Thus, to help prep you for the Rithm Capital SQL interview, we've collected 11 Rithm Capital SQL interview questions in this article.
Background: Rithm Capital is interested in understanding who their top investors are. A top investor can be defined as a user who has made the most investments over a certain period (e.g., the last quarter, the last year, etc.).
For this exercise, let's assume we want to identify the top 10 investors based on the amount invested in the last year.
You have been given two tables: and .
Many SQL problems start with two simple tables like this:
user_id | first_name | last_name | |
---|---|---|---|
1 | John | Doe | john@example.com |
2 | Jane | Smith | jane@example.com |
3 | Sam | Brown | sam@example.com |
4 | Alice | Johnson | alice@example.com |
5 | Bob | Davis | bob@example.com |
investment_id | user_id | investment_date | investment_amount |
---|---|---|---|
1001 | 1 | 2021-09-01 | 50000 |
1002 | 2 | 2021-05-01 | 40000 |
1003 | 3 | 2021-12-01 | 70000 |
1004 | 1 | 2022-01-01 | 20000 |
1005 | 2 | 2022-02-01 | 60000 |
You are asked to write a SQL query to identify the top 10 investors in terms of total investment amount in the last year.
This SQL query first joins the and tables based on the . It then filters the investments made in the last year. The aggregation (SUM) is performed on the investment amount per user. Finally, the result is ordered in descending order of total investment amount and limited to the top 10 users.
To practice a related super-user data analysis question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:
Learn about the leadership at Rithm Capital and understand what the company is all about.
Assume you had a table of Rithm Capital employee salary data. Write a SQL query to find the employees who earn more than their own manager.
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.
Test your SQL query for 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 code above is tough, you can find a detailed solution here: Employees Earning More Than Their Boss.
The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.The CHECK constraint is often used in conjunction with other constraints, such as NOT NULL or UNIQUE.
You might consider implementing the CHECK constraint in your database if you want to ensure that certain data meets specific conditions. This can be helpful for maintaining the quality and reliability of your data.
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.
Rithm Capital is interested in understanding the trading volume of its stocks. Within the table, there are fields for , , , , and where each row represents an individual trade transaction. Write a SQL query that calculates the average trading volume per month for each stock symbol.
trade_id | symbol | trade_date | volume | price |
---|---|---|---|---|
4532 | AAPL | 2022-02-25 00:00:00 | 500 | 150.00 |
7532 | AAPL | 2022-03-11 00:00:00 | 700 | 155.00 |
7681 | GOOG | 2022-02-23 00:00:00 | 200 | 2021.00 |
9534 | AAPL | 2022-03-30 00:00:00 | 600 | 153.00 |
1834 | MSFT | 2022-05-14 00:00:00 | 300 | 250.00 |
3213 | AAPL | 2022-04-20 00:00:00 | 400 | 157.00 |
7183 | GOOG | 2022-04-18 00:00:00 | 150 | 2015.00 |
8231 | MSFT | 2022-05-21 00:00:00 | 350 | 252.00 |
month | symbol | avg_volume |
---|---|---|
2 | AAPL | 500.00 |
2 | GOOG | 200.00 |
3 | AAPL | 650.00 |
4 | AAPL | 400.00 |
4 | GOOG | 150.00 |
5 | MSFT | 325.00 |
This query first uses the EXTRACT function to get the month from the . It then groups by both the month and symbol to calculate the average volume. It finally orders by month and symbol to give a clear overview of the average volume for each stock per month.
To solve a related window function SQL problem on DataLemur's free interactive coding environment, solve this Amazon SQL Interview Question:
Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.
Suppose Rithm Capital manages a wide range of investments for its clients and groups them into various risk categories. These investments are stored in an table. Each investment has an associated , , , and .
Your task is to write a SQL query to calculate the total invested amount for each .
|investment_id|client_id|investment_amount|risk_category| |:----|:----|:----|:----|:----| |101|1111|3000|High| |102|1112|2000|Medium| |103|1113|1000|Low| |104|1114|5000|High| |105|1115|3000|Low| |106|1116|4000|Medium| |107|1117|7000|High|
This query works by grouping the investments table by , and then for each group, it calculates the sum of to get the total invested amount for each risk category. This provides Rithm Capital with an overview of how much money clients have invested in each risk category, helping them better manage and evaluate their risk portfolio.
The clause is used to filter rows from the result set of a , , or statement. It allows you to specify a condition that must be met for a row to be included in the result set.
The clause is used to filter groups created by the clause. It is similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
Say you were working on a social media analytics project for Rithm Capital.
Here is an example of a SQL query that you might write which uses both the WHERE and HAVING clauses:
This query retrieves the total impressions and average conversions for each platform in the table, the date of the campaign is in January 2023. The rows are grouped by platform and the clause filters the groups to include only those with more than 5000 impressions and an average conversion rate above 0.2.
Rithm Capital, a successful private equity firm, has their investment portfolio spread across several sectors. For their internal analysis and strategic decisions, they want to understand on average, how much return on investment (ROI) they are getting for each sector. Can you write an SQL Query that calculates the average ROI for each sector?
Let's consider portfolio as an example input data table:
investment_id | sector | investment_date | investment_cost | current_value |
---|---|---|---|---|
101 | Technology | 12/17/2019 | 1000000 | 1500000 |
102 | Healthcare | 12/18/2019 | 500000 | 700000 |
103 | Technology | 01/08/2020 | 1500000 | 1800000 |
104 | Energy | 01/20/2020 | 2000000 | 2500000 |
105 | Healthcare | 02/26/2020 | 800000 | 750000 |
To calculate ROI, we first need to find the return for each investment by subtracting investment_cost from current_value. Then, ROI can be calculated by dividing return by investment_cost. The formula for ROI is [(current_value – investment_cost) / investment_cost] * 100.
This SQL command groups all the records by 'sector', calculates the ROI for each investment and then calculates the average ROI per sector. This is approximated to two decimal places for easy reading.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating group-based profit metrics or this Wayfair Y-on-Y Growth Rate Question which is similar for measuring return on investment over time.
For Rithm Capital, a digital financial products company, analyzing user interaction with their advertisements and product pages is pivotal to optimizing their marketing strategy and product placement. Recently, they have started tracking how many users viewed their product, clicked on it, and eventually added it to their virtual cart.
Rithm Capital would like to know the conversion rates from viewing a product, to clicking on it, to adding it to a cart for each of their products. This should be calculated as the number of adds to cart divided by the number of views. The data is stored in two tables: 'product_views' and 'cart_adds' as below:
view_id | user_id | view_date | product_id |
---|---|---|---|
1101 | 345 | 08/02/2022 00:00:00 | 100001 |
2202 | 478 | 08/03/2022 00:00:00 | 100002 |
3303 | 562 | 08/04/2022 00:00:00 | 100003 |
4404 | 785 | 08/05/2022 00:00:00 | 100002 |
5505 | 345 | 08/06/2022 00:00:00 | 100003 |
add_id | user_id | add_date | product_id |
---|---|---|---|
9901 | 345 | 08/03/2022 00:00:00 | 100001 |
8802 | 785 | 08/05/2022 00:00:00 | 100002 |
7703 | 345 | 08/06/2022 00:00:00 | 100003 |
6604 | 478 | 08/07/2022 00:00:00 | 100002 |
5505 | 562 | 08/08/2022 00:00:00 | 100003 |
Build a SQL query to provide the conversion rate for each product in an output table as follows:
product_id | conversion_rate |
---|---|
100001 | 1.0 |
100002 | 1.5 |
100003 | 1.5 |
With PostgreSQL, the solution can be achieved with subqueries. Here is a sample SQL query:
This query first groups 'product_views' and 'cart_adds' by 'product_id' and calculates the count in each table. Then it joins these two tables on 'product_id' and calculates the conversion rate by dividing the count of 'cart_adds' by the count of 'product_views'.
The output of this query provides the conversion rate from viewing a product to adding it to the cart for each product. This data allows Rithm Capital to understand user behavior and identify potential opportunities for optimization.
To solve a similar problem about calculating rates, try this TikTok SQL Interview Question within DataLemur's online SQL code editor:
The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.
Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't stress about knowing which DBMS supports what exact commands since the interviewers at Rithm Capital should be lenient!).
For a tangible example in PostgreSQL, suppose you were doing an HR Analytics project for Rithm Capital, and had access to Rithm Capital's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables. You could use operator to find all contractors who never were a employee using this query:
Rithm Capital is interested in determining how many investors have opted for a specific type of investment. The company wants you to find all the customers whose starts with "Com" from the database table.
customer_id | customer_name | join_date | investment_type |
---|---|---|---|
2345 | Sarah | 2018-10-02 | Commodities |
3876 | Peter | 2020-05-07 | Real Estate |
4692 | Megan | 2019-01-15 | Commodities |
5612 | John | 2021-02-20 | Equities |
6325 | Jennifer | 2020-07-08 | Community Bonds |
This query uses the keyword and the wildcard 'Com%' to select records from the table where starts with the string 'Com'. The '%' symbol enables you to perform pattern matching for any characters following 'Com'. In this case, the query would return customer records for customers with investment types of 'Commodities' and 'Community Bonds'.
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. In addition to solving the above Rithm Capital SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like FAANG and tech startups.
Each exercise has multiple hints, step-by-step solutions and most importantly, there is an online SQL coding environment so you can instantly run your SQL query answer and have it checked.
To prep for the Rithm Capital SQL interview you can also be helpful to practice SQL problems from other mortgage & loan companies like:
However, if your SQL foundations are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers things like UNION vs. joins and aggregate functions – both of these show up often in SQL interviews at Rithm Capital.
In addition to SQL query questions, the other types of problems covered in the Rithm Capital Data Science Interview are:
To prepare for Rithm Capital Data Science interviews read the book Ace the Data Science Interview because it's got: