10 Reinsurance Group of America SQL Interview Questions (Updated 2024)

Data Science, Data Engineering and Data Analytics employees at Reinsurance Group of America write SQL queries to extract insights from reinsured policy data, including policy coverage and claims history. It is also used to forecast potential risk scenarios using historical data insights, such as identifying high-risk policy segments, which is why Reinsurance Group of America often uses SQL questions during job interviews.

So, to help you prepare, here's 10 Reinsurance Group of America SQL interview questions – can you solve them?

10 Reinsurance Group of America SQL Interview Questions

SQL Question 1: Identifying Reinsurance Power Clients

As a data analyst for Reinsurance Group of America (RGA), you are asked to identify the top clients who contribute the most to premium payments every year. We classify a power client as a client who pays more than $1,000,000 in annual premiums.

Write a SQL query to find all power clients, their total annual premiums, and the number of policies they have.

We'll be basing off two hypothetical tables: clients and policies. The clients table has information about client ID and name, while the policies table has details about policy ID, client ID (linked to the clients table), premium paid in the year, and start year of the policy.

clients Example Input:

policies Example Input:

Answer:

SELECT c.client_id, c.client_name, SUM(p.annual_premium) as total_annual_premium, COUNT(p.policy_id) as total_policies
FROM clients c
JOIN policies p ON c.client_id = p.client_id
GROUP BY c.client_id, c.client_name
HAVING SUM(p.annual_premium) > 1000000
ORDER BY total_annual_premium DESC;

This query would return a result table with four columns: client_id, client_name, total_annual_premium and total_policies. 'total_annual_premiun' column represents the sum of all the premium payments each client made per year and 'total_policies' column provides the total number of policies each client has. The HAVING clause ensures that only those clients who have paid more than $1,000,000 in annual premiums are included, while ORDER BY clause arranges them in descending order of the total premiums paid, with the client who paid the most at the top.

To work on another SQL customer analytics question where you can solve it right in the browser and have your SQL query instantly graded, try this Walmart Labs SQL Interview Question:

SQL Question 2: Highly-Paid Employees

Given a table of Reinsurance Group of America employee salary information, write a SQL query to find employees who make more money than their direct boss.

Reinsurance Group of America employees Example Input:

Example Output:

This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.

Check your SQL query for this problem and run your code right in DataLemur's online SQL environment:

Answer:

First, we perform a SELF-JOIN where we treat the first employee table (mgr) as the managers' table and the second employee table (emp) as the employees' table. Then we use a WHERE clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.

SELECT
  emp.employee_id AS employee_id,
  emp.name AS employee_name
FROM employee AS mgr
INNER JOIN employee AS emp
  ON mgr.employee_id = emp.manager_id
WHERE emp.salary > mgr.salary;

If the solution above is tough, you can find a detailed solution here: Well Paid Employees.

SQL Question 3: What do the SQL commands EXCEPT / MINUS do?

Note: interviews at Reinsurance Group of America often aren't trying to test you on a specific flavor of SQL. As such, you don't need to exactly know that EXCEPT is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle – you just need to know the general concept!

Your answer should mention that the MINUS/EXCEPT operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.

Here's a PostgreSQL example of using EXCEPT to find all of Reinsurance Group of America's Facebook video ads with more than 50k views that aren't also being run on YouTube:

SELECT ad_creative_id
FROM reinsurance_group_of_america_facebook_ads
WHERE views > 50000 AND type=video

EXCEPT

SELECT ad_creative_id
FROM reinsurance_group_of_america_youtube_ads

If you want to retain duplicates, you can use the EXCEPT ALL operator instead of EXCEPT. The EXCEPT ALL operator will return all rows, including duplicates.

SQL Question 4: Analyzing Premium Amount Per Policy Type

As an actuarial analyst for Reinsurance Group of America, you are tasked with analyzing the total premium amount collected per policy type on a quarterly basis for the year 2022. The aim is to understand the trend in premium collection for each policy type within each quarter of the year.

For this task, you have been provided with a policies table and a premiums table.

policies Example Input:

premiums Example Input:

Answer:

Consider the query below:

WITH premiums_quarterly AS (
  SELECT policy_id,
         EXTRACT(QUARTER FROM payment_date) as quarter,
         Sum(amount) as total_premium
  FROM premiums
  WHERE EXTRACT(YEAR FROM payment_date) = 2022
  GROUP BY policy_id, quarter
)

SELECT p.policy_type,
       pq.quarter,
       Sum(pq.total_premium) OVER (PARTITION BY p.policy_type ORDER BY pq.quarter) as running_total
FROM premiums_quarterly pq
JOIN policies p ON pq.policy_id = p.policy_id
ORDER BY p.policy_type, pq.quarter;

This uses a window function to calculate and display the cumulative total premiums per policy type per quarter. The query first groups the premiums by policy ID and quarter, then joins that with the policy information to get the policy type. It uses a window function to sum the total_premium for each policy type, partitioned by policy type and ordered by quarter.

p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur

SQL Question 5: What's the difference between a left and right join?

A join in SQL combines rows from two or more tables based on a shared column or set of columns. To demonstrate the difference between a LEFT JOIN and RIGHT JOIN, say you had a table of Reinsurance Group of America orders and Reinsurance Group of America customers.

LEFT JOIN: A LEFT JOIN retrieves all rows from the left table (in this case, the Orders table) and any matching rows from the right table (the Customers table). If there is no match in the right table, NULL values will be returned for the right table's columns.

RIGHT JOIN: A RIGHT JOIN retrieves all rows from the right table (in this case, the Customers table) and any matching rows from the left table (the Orders table). If there is no match in the left table, NULL values will be returned for the left table's columns.

SQL Question 6: Policy and Claims Analysis

As a data analyst at Reinsurance Group of America, you are given the task to analyze the insurance claims made during a particular year. You are required to find out the total number of policies under each type of plan and the total claims made under each plan for that year. Consider the policies table and claims table given below.

policies Example Input:

claims Example Input:

Answer:

WITH policy_counts AS (
  SELECT plan_type, COUNT(*) as total_policies
  FROM policies
  WHERE EXTRACT(YEAR FROM issue_date) = 2020
  GROUP BY plan_type),
claim_counts AS (
  SELECT p.plan_type, COUNT(*) as total_claims
  FROM claims c
  JOIN policies p ON c.policy_id = p.policy_id
  WHERE EXTRACT(YEAR FROM c.claim_date) = 2020
  GROUP BY p.plan_type)
SELECT pc.plan_type, pc.total_policies, cc.total_claims
FROM policy_counts pc
JOIN claim_counts cc ON pc.plan_type = cc.plan_type;

In this solution, two named subqueries are created using WITH clause named policy_counts and claim_counts to get the policy count and claim count respectively for each plan in the year 2020. Then, these two subqueries are joined using a JOIN clause to combine the respective counts based on the plan_type. The resulting table will represent the total number of policies under each plan (total_policies) and the corresponding total claims made under that plan for the year 2020 (total_claims).

SQL Question 7: Describe the difference between UNION and UNION ALL.

The UNION operator merges the output of two or more SELECT statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.

For a concrete example, say you were a Data Analyst at Reinsurance Group of America working on a Marketing Analytics project. If you needed to get the combined result set of both Reinsurance Group of America's Google and Facebook ads you could execute this SQL query:

SELECT ad_name
FROM reinsurance_group_of_america_google_ads;

UNION

SELECT ad_name
FROM reinsurance_group_of_america_facebook_ads;

The UNION ALL operator works in a similar way to combine data from multiple SELECT statements, but it differs from the UNION operator when it comes to handling duplicate rows. Whereas UNION filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), UNION ALL outputs duplicate rows.

SQL Question 8: Calculate the Click-through Conversion Rate for Ad Campaigns

Reinsurance Group of America is interested in understanding the effectiveness of their ad campaigns. They want to know the click-through conversion rate of their ads. This rate is defined as the number of users who click on an ad and subsequently take a specific action (i.e., subscribe to a newsletter) divided by the total number of users who clicked on the ad.

Consider the following sample data from two tables, ad_clicks and ad_subscriptions.

ad_clicks Example Input:

ad_subscriptions Example Input:

We want to calculate the click-through conversion rate per ad.

Answer:

SELECT ad_clicks.ad_id,
       COUNT(DISTINCT ad_subscriptions.user_id)::FLOAT / COUNT(DISTINCT ad_clicks.user_id) AS conversion_rate
FROM ad_clicks
LEFT JOIN ad_subscriptions ON ad_clicks.user_id = ad_subscriptions.user_id 
                            AND ad_clicks.ad_id = ad_subscriptions.ad_id
GROUP BY ad_clicks.ad_id;

This query first joins the ad_clicks and ad_subscriptions tables based on user_id and ad_id. It then groups by ad_id and calculates the conversion rate as the number of subscriptions (unique user_ids in ad_subscriptions) divided by the number of clicks (unique user_ids in ad_clicks). The result is the click-through conversion rate for each ad.

To practice a similar SQL interview question on DataLemur's free online SQL code editor, solve this SQL interview question asked by Facebook:

SQL Question 9: Average Claim Amount Per Policy Holder Per Year

Suppose you are a data analyst for Reinsurance Group of America and they provide you with a database containing information about insurance policies and the related claims. Your task is to write a query that calculates the average claim amount per policy holder per year.

The databases used for this task are policiesand claims tables. Here are the expected structures and examples of entries in these tables.

policies Example Input:

claims Example Input:

Answer:

SELECT 
    EXTRACT(YEAR FROM c.claim_date) AS claim_year,
    p.holder_id,
    AVG(c.claim_amount) AS avg_claim_amount
FROM 
    claims c
JOIN 
    policies p
ON 
    p.policy_id = c.policy_id
GROUP BY 
    claim_year, p.holder_id
ORDER BY 
    claim_year, avg_claim_amount DESC;

This query will first join the claims and policies tables on policy_id. Then, it groups the data by year and holder_id and calculates the average claim amount for each group. The final results are ordered by year and the average claim amount in descending order to give you a clear view of the holders who have the highest average claim amount each year.

SQL Question 10: What's the difference between a correlated and 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.

Preparing For The Reinsurance Group of America SQL Interview

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. Beyond just solving the above Reinsurance Group of America SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).

Each problem on DataLemur has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there's an online SQL coding environment so you can right in the browser run your SQL query and have it checked.

To prep for the Reinsurance Group of America SQL interview you can also be useful to solve interview questions from other insurance companies like:

• Lincoln National SQL questions
• Nationwide SQL questions
• Aflac SQL questions
• The Hartford SQL questions

Discover how RGA's data and analytics solutions can help you make data-driven decisions and drive business success!

However, if your SQL coding skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this free SQL tutorial.

This tutorial covers SQL concepts such as LEAD/LAG window functions and SUM/AVG window functions – both of these pop up frequently in SQL interviews at Reinsurance Group of America.

Reinsurance Group of America Data Science Interview Tips

What Do Reinsurance Group of America Data Science Interviews Cover?

Besides SQL interview questions, the other types of problems to prepare for the Reinsurance Group of America Data Science Interview include:

• Experimental Design & AB Testing Questions
• Python or R Programming Questions
• Open-Ended Data Case Studies
• Machine Learning Questions
• Behavioral Questions focussed on Reinsurance Group of America values & principles

How To Prepare for Reinsurance Group of America Data Science Interviews?

To prepare for Reinsurance Group of America Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google)
• a refresher on Stats, SQL & ML
• over 1000+ 5-star reviews on Amazon

Also focus on the behavioral interview – prepare for that with this list of common Data Scientist behavioral interview questions.