10 American Financial Group SQL Interview Questions (Updated 2024)
American Financial Group employees use SQL to analyze financial data trends by querying large datasets to identify patterns and correlations, as well as to optimize insurance pricing models by creating data visualizations and performing statistical analysis. For this reason, American Financial Group frequently asks SQL problems in interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help prep you for the American Financial Group SQL interview, we've collected 10 American Financial Group SQL interview questions – can you solve them?
10 American Financial Group SQL Interview Questions
SQL Question 1: Identify the Top Investors based on Transaction Value
As a data analyst for the American Financial Group, your task is to identify the top investors within the company's database. These investors, also known as ‘whale’ users, frequently engage in high-value financial transactions and are thus crucial for the business. Write a SQL query that lists the top 10 investors (user_id) based on the total value of their transactions, also including their total transaction counts and their names.
Let's assume we have a users table and a transactions table:
Sample Data:
| user_id | name |
|---|---|
| 94372 | Henry |
| 87462 | Emma |
| 59630 | Sofia |
| 39287 | Daniel |
| 24930 | Oliver |
Sample Data:
| tran_id | user_id | tran_date | value |
|---|---|---|---|
| 1010 | 94372 | 02/01/2022 00:00:00 | 20000 |
| 1021 | 87462 | 02/10/2022 00:00:00 | 15000 |
| 1083 | 59630 | 02/15/2022 00:00:00 | 25000 |
| 1072 | 39287 | 03/01/2022 00:00:00 | 10000 |
| 1019 | 24930 | 03/05/2022 00:00:00 | 5000 |
Answer:
This PostgreSQL query will join the users table and the transactions table on user_id and aggregate the total value of transactions as well as count the total number of transactions for each user. It sorts the users in descending order by the total value of transactions, and limits the result to the top 10.
To work on a similar customer analytics SQL question where you can solve it interactively and have your SQL query automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: 2nd Highest Salary
Suppose you had a table of American Financial Group employee salary data. Write a SQL query to find the 2nd highest salary at the company.
American Financial Group Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
You can solve this problem and run your code right in the browser:
Answer:
You can find a step-by-step solution here: 2nd Highest Salary.
SQL Question 3: Could you describe the function of UNION in SQL?
The operator merges the output of two or more statements into a single result set. The two SELECT statements within the UNION must have the same number of columns and the data types of the columns are all compatible.
For example, if you were a Data Analyst on the marketing analytics team at American Financial Group, this statement would return a combined result set of both American Financial Group's Google and Facebook ads that have more than 300 impressions:
SQL Question 4: Analyzing Employee Sales Performance
As a data scientist at American Financial Group, we would like you to analyze the sales performance of our employees over time. Specifically, we need a SQL query to calculate each employee's cumulative sales over time, on monthly basis.
Consider the following table:
Example Input:
| sales_id | employee_id | sales_date | product_id | amount |
|---|---|---|---|---|
| 101 | 1 | 2022-01-05 | 1001 | 5000 |
| 102 | 1 | 2022-02-15 | 1002 | 6000 |
| 103 | 2 | 2022-01-10 | 1001 | 7000 |
| 104 | 2 | 2022-02-20 | 1001 | 8000 |
| 105 | 3 | 2022-03-25 | 1002 | 9000 |
We would like to know the cumulative sales for each employee up to each month.
Example Output:
| year_month | employee_id | cumulative_sales |
|---|---|---|
| 2022-01 | 1 | 5000 |
| 2022-02 | 1 | 11000 |
| 2022-01 | 2 | 7000 |
| 2022-02 | 2 | 15000 |
| 2022-03 | 3 | 9000 |
For the above SQL question, write a PostgreSQL query to solve it.
Answer:
Here is the SQL query to solve the question:
This query first formats the to get the year and month into a column. We then partition our data by and order by . For each partition, PostgreSQL will calculate the running total (cumulative sum) of the , giving us the cumulative sales for each employee up to each month.
To practice a similar window function interview problem which uses RANK() on DataLemur's free online SQL coding environment, solve this Amazon BI Engineer interview question:
SQL Question 5: What is a cross-join?
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
Suppose you were building a Neural Network ML model, that tried to score the probability of a customer buying a American Financial Group product. Before you started working in Python and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and American Financial Group products.
Here's a cross-join query you could use to find all the combos:
Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. For example, if you had 10,000 potential customers, and American Financial Group had 500 different product SKUs, you'd get 5 million rows as a result!!
SQL Question 6: Average Premium of Policies by State
As a data analyst at American Financial Group, you have to often analyze different policies data and present insights. One such task is to calculate and report the average premium of all the active policies grouped by their issuing state. The data for this problem resides in two tables - and .
Example Input:
| policy_id | state | start_date | end_date | premium |
|---|---|---|---|---|
| 7350 | California | 06/08/2019 | 06/08/2020 | 500 |
| 5486 | New York | 06/12/2019 | 06/11/2020 | 750 |
| 6732 | California | 06/18/2019 | 06/17/2020 | 600 |
| 7412 | Texas | 07/05/2019 | 07/04/2020 | 350 |
| 5319 | New York | 07/10/2019 | 07/09/2020 | 400 |
Example Input:
| claim_id | policy_id | claim_date | claim_amount |
|---|---|---|---|
| 3421 | 7350 | 12/12/2019 | 200 |
| 2958 | 5486 | 01/01/2020 | 600 |
| 3685 | 6732 | 02/07/2020 | 300 |
| 4212 | 7412 | 03/05/2020 | 150 |
| 5168 | 5319 | 04/10/2020 | 400 |
Write a SQL query in PostgreSQL that outputs the average premium amount by state for active policies. An active policy is one where today's date falls within the start and end dates of the policy.
The output of your query should look like this:
Example Output:
| state | avg_premium |
|---|---|
| California | $550.00 |
| New York | $575.00 |
| Texas | $350.00 |
Answer:
In this query, I joined the policies and claims tables on the policy id. I restricted the policies to those which are currently active by checking that today's date falls between the start and end dates of the policy. Then, I grouped the results by state and calculated the average premium for each group. Note that AVG is a built-in PostgreSQL function that calculates the average of a set of values.
To practice a very similar question try this interactive CVS Health Pharmacy Analytics (Part 1) Question which is similar for calculating profits by grouping or this Amazon Average Review Ratings Question which is similar for calculating averages by grouping.
SQL Question 7: What does do, and when would you use this function?
The COALESCE() function can be used to replace NULL values with a specified value. For instance, if you are a Data Analyst at a company and are working on a customer analytics project to identify the most engaged customers, you may have access to the following data in the table:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | very_active |
| 601 | NULL | NULL |
Before you could procede with your analysis, you would need to remove the NULLs, and replace them with the default value for email engagement (not_active), and the default sms_engagement level (not_opted_in).
To do this, you'd run the following query:
This would get you the following output:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 102 | un-subscribed | not_opted_in |
| 103 | not_active | not_opted_in |
| 104 | not_active | very_active |
| 105 | very_active | very_active |
| 106 | not_active | not_opted_in |
SQL Question 8: Analysing Click-through-rates for American Financial Group
American Financial Group is interested in deep diving into their digital marketing efforts. Their primary key performance indicator (KPI) is the click-through rate (CTR). The CTR is calculated as the number of users who click on an ad divided by the number of total users who viewed the ad.
They are also interested in their click-through conversion rate, which they define as the number of users who clicked on the ad and actually purchased a policy, divided by the number of total users who clicked the ad.
They want to analyze these rates for the last quarter on a monthly basis.
Table:
| impression_id | user_id | impression_date | ad_id |
|---|---|---|---|
| 105245 | 123 | 04/02/2022 00:00:00 | 501 |
| 210834 | 456 | 04/10/2022 00:00:00 | 502 |
| 301890 | 789 | 06/25/2022 00:00:00 | 501 |
| 410573 | 612 | 06/30/2022 00:00:00 | 502 |
Table:
| click_id | user_id | click_date | ad_id |
|---|---|---|---|
| 89018 | 123 | 04/02/2022 00:00:00 | 501 |
| 72107 | 456 | 05/09/2022 00:00:00 | 502 |
Table:
| purchase_id | user_id | purchase_date |
|---|---|---|
| 96341 | 123 | 04/03/2022 00:00:00 |
| 77256 | 456 | 05/10/2022 00:00:00 |
Answer:
In this analysis, we first aggregate impression, click and purchase events to a monthly level. The click-through-rate and the click-through-conversion-rate are then computed by dividing the respective count values. Note that click-through-rate is calculated per ad while click-through-conversion-rate is calculated for the overall campaign.
To solve a similar SQL interview question on DataLemur's free online SQL code editor, try this Meta SQL interview question:
SQL Question 9: Calculate Total Premiums by State and Policy Type
As a member of the data team at the American Financial Group, you are tasked with analyzing the company's portfolio of insurance policies. You are asked to write a SQL query that obtains the sum of premiums grouped by state and policy type.
The table contains the following fields:
- (integer): The unique identifier of the policy
- (integer): The unique identifier of the policy holder
- (text): The US state where the policy holder resides
- (text): The type of insurance policy (e.g., Auto, Homeowners, Life, etc.)
- (integer): The annual premium amount for the policy in USD
Example Input:
| policy_id | holder_id | state | policy_type | premium |
|---|---|---|---|---|
| 101 | 9901 | CA | Auto | 1200 |
| 202 | 9802 | NY | Homeowners | 2100 |
| 303 | 9703 | FL | Life | 800 |
| 404 | 9604 | NY | Auto | 1100 |
| 505 | 9505 | CA | Life | 700 |
| 606 | 9406 | FL | Auto | 1300 |
You want to generate an output table that shows the total annual premiums for each type of policy grouped by state.
Example Output:
| state | policy_type | total_premium |
|---|---|---|
| CA | Auto | 1200 |
| CA | Life | 700 |
| FL | Auto | 1300 |
| FL | Life | 800 |
| NY | Auto | 1100 |
| NY | Homeowners | 2100 |
Answer:
In this query, we are grouping the data by and columns. Then, for each group, we are calculating the sum of the column, which gives us the total premium for every combination of state and policy type. Finally, the result is ordered by the and columns to facilitate easier reading of the output.
SQL Question 10: How is the constraint used in a database?
A is a column or set of columns in a table that references the primary key of another table. It is used to link the two tables together, and to ensure that the data in the foreign key column is valid.
The constraint helps to ensure the integrity of the data in the database by preventing the insertion of rows in the foreign key table that do not have corresponding entries in the primary key table. It also helps to enforce the relationship between the two tables, and can be used to ensure that data is not deleted from the primary key table if there are still references to it in the foreign key table.
For example, if you have a table of American Financial Group customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the American Financial Group customers table.
American Financial Group SQL Interview Tips
The best way to prepare for a American Financial Group SQL interview is to practice, practice, practice. Besides solving the earlier American Financial Group SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each DataLemur SQL question has multiple hints, step-by-step solutions and crucially, there's an interactive coding environment so you can easily right in the browser your SQL query and have it executed.
To prep for the American Financial Group SQL interview it is also a great idea to solve SQL problems from other insurance companies like:
- LPL Financial SQL questions
- Genworth SQL questions
- Gallagher US SQL questions
- FM Global SQL questions
Explore the latest happenings at American Financial Group and discover what's driving their success!
However, if your SQL query skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers things like SUM/AVG window functions and transforming strings with CONCAT()/LOWER()/TRIM() – both of these pop up frequently in American Financial Group SQL interviews.
American Financial Group Data Science Interview Tips
What Do American Financial Group Data Science Interviews Cover?
In addition to SQL interview questions, the other types of problems covered in the American Financial Group Data Science Interview include:
- Experimental Design & AB Testing Questions
- Coding Questions in Python or R
- Product-Sense Questions
- Machine Learning Questions
- Behavioral Based Interview Questions
How To Prepare for American Financial Group Data Science Interviews?
I think the best way to prepare for American Financial Group Data Science interviews is to read the book Ace the Data Science Interview.
It has 201 data interview questions sourced from FAANG (FB, Apple, Amazon, Netflix, Google). The book's also got a refresher on Stats, ML, & Data Case Studies. And finally it's helped a TON of people, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.
While the book is more technical in nature, it's also crucial to prepare for the American Financial Group behavioral interview. A good place to start is by reading the company's culture and values.