8 Annaly Capital Management SQL Interview Questions (Updated 2024)
Data Analytics, Data Science, and Data Engineering employees at Annaly Capital Management write SQL queries all the time as part of their job. They use SQL for analyzing financial data, spotting trends, and managing risk assessment. That's why Annaly Capital Management LOVES to ask folks interviewing at the company SQL interview questions.
So, to help you study, here's 8 Annaly Capital Management SQL interview questions – can you solve them?
8 Annaly Capital Management SQL Interview Questions
SQL Question 1: Calculate the Quarterly Return for Each Asset
As a data analyst at Annaly Capital Management, which is a real estate investment trust, one primary task is to monitor the performance of different assets. Suppose we have two tables, and .
The table contains the following columns: (asset_id), , and . The table contains , , and .
Your task is to write a query that calculates the cumulative return for each asset in every quarter of the year 2022.
Here is some sample data for the task:
Example Input:
| id | asset_name | sector |
|---|---|---|
| 1 | Asset A | Real Estate |
| 2 | Asset B | Real Estate |
| 3 | Asset C | Real Estate |
| 4 | Asset D | Real Estate |
Example Input:
| asset_id | date | return |
|---|---|---|
| 1 | 2022-01-01 | 0.05 |
| 1 | 2022-01-15 | 0.02 |
| 1 | 2022-02-10 | -0.01 |
| 2 | 2022-01-05 | 0.03 |
| 2 | 2022-02-15 | 0.04 |
| 2 | 2022-03-10 | 0.02 |
| 3 | 2022-02-01 | -0.01 |
| 3 | 2022-02-10 | 0.04 |
| 4 | 2022-03-05 | 0.02 |
| 4 | 2022-03-15 | 0.03 |
Answer:
The query joins the and tables on asset id. Then it uses a window function to calculate the cumulative return for each asset in every quarter of the year. The window function sums the returns per asset, per quarter. The WHERE clause limits the data to the year 2022. The result is a list of assets with their quarterly cumulative return.
To solve a similar window function interview problem which uses RANK() on DataLemur's free interactive coding environment, try this Amazon BI Engineer interview question:
SQL Question 2: Highly-Paid Employees
Imagine there was a table of Annaly Capital Management employee salary data. Write a SQL query to find the employees who earn more than their direct manager.
Annaly Capital Management 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.
Try this interview question directly within the browser 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 solution above is tough, you can find a step-by-step solution with hints here: Employees Earning More Than Their Boss.
SQL Question 3: Can you explain the distinction between a correlated and a non-correlated sub-query?
A correlated sub-query is one that is linked to the outer query and cannot be executed on its own. It uses the outer query to filter or transform data by referencing a column from the outer query, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is independent of the outer query and can be executed on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.
Correlated sub-queries are slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
SQL Question 4: Calculate Click-Through Conversion Rate for Annaly Capital Management's Digital Products
Annaly Capital Management is a company that sells a variety of financial products. Like many companies, they also have a digital presence and sell some of their products online. When a customer visits the website, they might view a product page. Sometimes, they may add the product to their cart. The company is interested in understanding the conversion rate from viewing a product to adding it to the user's cart. Calculate the click-through conversion rate for each product.
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 101 | 123 | 06/08/2022 | 50001 |
| 102 | 265 | 06/10/2022 | 69852 |
| 103 | 362 | 06/18/2022 | 50001 |
| 104 | 192 | 07/26/2022 | 69852 |
| 105 | 981 | 07/26/2022 | 50001 |
Example Input:
| cart_id | user_id | cart_date | product_id |
|---|---|---|---|
| 201 | 123 | 06/08/2022 | 50001 |
| 202 | 265 | 07/01/2022 | 69852 |
| 203 | 362 | 06/18/2022 | 50001 |
| 204 | 192 | 07/27/2022 | 69852 |
Answer:
This query works by first joining the page_views and carts tables on both user_id and product_id. It then groups the resulting rows by product_id. For each product_id, it computes the conversion rate as the ratio of users who viewed the product page and added the product to their cart (the numerator) to the total number of users who viewed the product page (the denominator). We use the DISTINCT keyword in the COUNT function to ensure we count each user only once, which avoids overestimating the conversion rate if a user views a product page or adds a product to their cart multiple times. The conversion rate is reported as a decimal.
To solve another question about calculating rates, solve this TikTok SQL question on DataLemur's online SQL coding environment:
SQL Question 5: In SQL, are blank spaces the same as a NULL?
A value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values.
It's important to handle values properly in SQL because they can cause unexpected results if not treated correctly. For example, comparisons involving values always result in . Also values are not included in calculations. For example, will ignore values in the column.
SQL Question 6: Average Monthly Mortgage Amounts
Annaly Capital Management specialises in the management and financing of mortgage-backed securities. This query is set in the context of their business. You run their database of all the loans they have issued, and the question is to find the average mortgage amount by month and year, based on the issued date of each mortgage.
Example Input:
| loan_id | customer_id | issued_date | mortgage_amount |
|---|---|---|---|
| 2197 | 8345 | 06/08/2021 00:00:00 | 325000 |
| 6518 | 5217 | 06/17/2021 00:00:00 | 120000 |
| 8974 | 9385 | 07/20/2021 00:00:00 | 275000 |
| 5892 | 7201 | 07/08/2021 00:00:00 | 220000 |
| 7541 | 3918 | 07/02/2021 00:00:00 | 180000 |
Example Output:
| year | month | avg_loan_amount |
|---|---|---|
| 2021 | 6 | 222500 |
| 2021 | 7 | 225000 |
Answer:
In this query, we're using the function to get the year and month from the in each row of the table. We then group by those year and month and calculate the average for each group. Finally, we order by the year and month for clarity.
SQL Question 7: What is database denormalization, and when is it a good idea to consider it?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
A few reasons to denormalize a database:
-
Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases at Annaly Capital Management, as joins can be expensive and slow.
-
Scalability: Denormalization can be like a turbo boost for your database's scalability! By requiring less data to be read and processed when executing a query, denormalization can help your database handle a ton of queries without breaking a sweat.
-
Simplification: One way to simplify the design of a database is by using denormalization to reduce the number of tables and relationships that need to be managed. This can make the database easier to understand and maintain.
Of course, don't over-do the database denormalization magic – too much redundancy can increase data storage costs, and get complicated to manage if there's frequent commands that need to be run.
REad about Annaly's Investor relations and thier 2024 earnings press release.
SQL Question 8: Calculate the Compound Interest
As the financial data analyst at Annaly Capital Management, you're tasked with evaluating the potential investment returns on a given property portfolio each investor has. For each property, there is an estimated annual profit rate. The company uses a compound interest model to calculate investment returns.
To simplify the question, let's ignore issues like tax, appreciation, etc. Write a SQL query to calculate compound interest for each portfolio, rounded to two decimal places.
Example Input:
| property_id | property_name | annual_profit_rate |
|---|---|---|
| 1 | Property A | 0.05 |
| 2 | Property B | 0.03 |
| 3 | Property C | 0.04 |
Example Input:
| investment_id | investor_id | property_id | invested_value | investment_years |
|---|---|---|---|---|
| 1 | John | 1 | 12000 | 5 |
| 2 | Emily | 2 | 15000 | 3 |
| 3 | Mitch | 3 | 18000 | 4 |
| 4 | John | 2 | 20000 | 2 |
| 5 | Emily | 1 | 15000 | 1 |
Answer:
The above SQL script uses a JOIN statement to combine the investment and property tables. It then utilizes the POWER function to calculate the compound interest for each investment, and uses the ROUND function to ensure that the final investment value will be in a format that's easy to read and understand. The final value is calculated by the following formula: .
The two most similar questions to your SQL compound interest query are "Y-on-Y Growth Rate" and "App Click-through Rate (CTR)". Both problems involve calculations over time which is similar to the compound interest problem.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating growth over time or this Facebook App Click-through Rate (CTR) Question which is similar for calculating rates over time.
Preparing For The Annaly Capital Management SQL Interview
The key to acing a Annaly Capital Management SQL interview is to practice, practice, and then practice some more!
In addition to solving the above Annaly Capital Management SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Microsoft, Google, Amazon, and tech startups.
Each problem on DataLemur has multiple hints, step-by-step solutions and crucially, there's an online SQL code editor so you can easily right in the browser your SQL query and have it graded.
To prep for the Annaly Capital Management SQL interview it is also wise to practice SQL problems from other real estate and REIT companies like:
But if your SQL coding skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers SQL concepts such as LAG window function and window functions like RANK() and ROW_NUMBER() – both of which come up routinely during Annaly Capital Management SQL assessments.
Annaly Capital Management Data Science Interview Tips
What Do Annaly Capital Management Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions tested in the Annaly Capital Management Data Science Interview are:
- Probability & Stats Questions
- Coding Questions in Python or R
- Open-Ended Data Case Studies
- Machine Learning and Predictive Modeling Questions
- Behavioral Interview Questions
How To Prepare for Annaly Capital Management Data Science Interviews?
The best way to prepare for Annaly Capital Management Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Facebook, Google & startups
- A Refresher on Python, SQL & ML
- Great Reviews (1000+ reviews, 4.5-star rating)