10 Mutual of Omaha SQL Interview Questions (Updated 2024)

Data Analytics, Data Science, and Data Engineering employees at Mutual of Omaha uses SQL to analyze policyholder datasets, including claims history and demographic data, to identify trends. It is also used to improve the underwriting process by refining risk assessment models, like predicting policy lapse rates, which is why Mutual of Omaha often asks jobseekers SQL interview problems.

So, to help you prepare for the Mutual of Omaha SQL interview, here's 10 Mutual of Omaha Insurance SQL interview questions – can you solve them?

10 Mutual of Omaha Insurance SQL Interview Questions

SQL Question 1: Identify Power Policyholders for Mutual of Omaha

For insurance companies like Mutual of Omaha, power users (also known as VIP or premium users) could be those policyholders who hold multiple policies and often update their policies. Let's say for the scope of this question, power users are the users who hold more than 4 policies and renew their policies at least once a year.

Given two tables, table which has columns for , , , , , , ; and table which has columns for , , , , , .

Example Input:

Example Input:

Construct a SQL query to identify these power users by pulling their user_id, user_name and count of policies.

Answer:

The PostgreSQL query for the above question could be:

This SQL query joins the and table based on the . Then it aggregates the data by and counts the policy they hold. The clause is used to filter out the users who hold more than 4 policies and have renewed any of their policies within the last year. Therefore, this query return the power users based on the defined criteria.

To practice a super-customer analysis question on DataLemur's free online SQL coding environment, try this Microsoft SQL Interview problem:

SQL Question 2: Top 3 Department Salaries

Assume there was a table of Mutual of Omaha employee salary data. Write a SQL query to find the top 3 highest earning employees within each department.

Mutual of Omaha Example Input:

Example Input:

Example Output:

Code your solution to this interview question interactively on DataLemur:

Answer:

We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.

If the solution above is confusing, you can find a step-by-step solution here: Top 3 Department Salaries.

SQL Question 3: What are database views, and when would you use them?

Database views are created to provide customized, read-only versions of your data that you can query just like a regular table. So why even use one if they're just like a regular table?

Views are useful for creating a simplified version of your data for specific users, or for hiding sensitive data from certain users while still allowing them to access other data.

SQL Question 4: Calculate the Average Premium By Month for Different Policy Types

At Mutual of Omaha, we offer different policy types to our customers such as life insurance, health insurance, long-term care insurance etc. As part of our regular analysis, we often need to compute the average premium, per month, for our different policy types.

Could you write an SQL query that calculates the average premium collected by month for different policy types?

Example Input:

Example Output:

Answer:

This query extracts the month from the start_date and calculates the average premium for each policy type. The clause combined with the clause facilitates the computation of the average premium, per policy type, separating the data by month. The clause helps in displaying this data sorted ascendingly by the month first and then by the policy type.

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 denormalization, and when does it make sense to do it?

Denormalization is the process of modifying a database schema in a way that deviates from the typical rules of normalization (1NF, 2NF, 3NF, etc.). There's a few reasons to denormalize a database:

Improved performance: Joins are slow AF, especially when dealing with the massive datasets that are typically used at Mutual of Omaha. Denormalization can improve the performance of a database by reducing the number of joins that are required to retrieve data. This can be particularly useful when the database is being used for querying and reporting purposes, as joins can be expensive and slow.

Scalability: By reducing the amount of data that needs to be read and processed to execute a query, denormalization can enhance the scalability of a database. This can be useful when the database is anticipated to handle a large number of read-only queries (such as in OLAP use cases).

Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.

Because denormalization can create more complex update and delete operations, and pose potential data integrity issues, consider denormalization only if joins are causing performance bottlenecks.

SQL Question 6: Customer and Policy Management

Mutual of Omaha is an insurance company which provides various types of policies to its customers. They are facing a challenge of efficiently managing the customers and their corresponding policies. As a Database Analyst, design a relational database such that it could store following information:

1. Customer details like customer ID, name, address, phone number etc.
2. Policy details like policy ID, policy type, policy start date, policy end date, policy cost etc.
3. A customer can have multiple policies and each policy can be owned by multiple customers.

Also, write a SQL query to find the customers who have more than one policy.

Example Input:

Example Input:

Example Input:

Answer:

We can write a SQL query using PostgreSQL to find the customers who have more than one policy. Here is the query:

This query first joins the table with the table using an INNER JOIN on . Then it groups the resulting data by and and finally applies the clause to filter the groups which have more than one policy. So the resulting output of this query will be the customers who own more than one policy.

SQL Question 7: What's the difference between an inner and a full outer join?

A full outer join returns all rows from both tables, including any unmatched rows, whereas an inner join only returns rows that match the join condition between the two tables.

For a tangible example, suppose you had a table of Mutual of Omaha orders and Mutual of Omaha customers.

Here's a SQL inner join using the orders and customers tables:

This query will return rows from the orders and customers tables that have matching values. Only rows with matching values will be included in the results.

Here is an example of a using the orders and customers tables:

This query will return all rows from both the orders and customers tables, including any rows that do not have matching values. Any rows with null values for either table will be included in the results.

SQL Question 8: Find the Average Amount of Client Coverage

As a Data Analyst for Mutual of Omaha, a company that offers life insurance, long-term care insurance, and disability insurance among other things, you are tasked to find the average amount of coverage for each type of insurance product based on client data.

Example Input:

Example Output:

Answer:

This PostgreSQL query calculates the average coverage amount for each type of product. The AVG function is used to find the average of the 'coverage_amount' column, with the results grouped by 'product_type'. The result gives the average coverage amount for each type of insurance product, which can help the company understand common coverage amounts chosen by clients.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average grouped by category or this Alibaba Compressed Mean Question which is similar for finding the mean of a particular column.

SQL Question 9: Find the Average Claim Amount per Month for Different Insurance Types

Mutual of Omaha provides multiple types of Insurance policies like Health, Life, etc. We would like to analyze the average claim amount filed by customers for each type of insurance every month. Can you write a SQL query that calculates the average claim amounts per month, disaggregated by Insurance type?

Here's some sample data for the problem:

Example Input:

Example Output:

Answer:

This query works by extracting the month from the column and then grouping by this extracted month and . We use the function to calculate the average for each group. The final output is ordered by and for easy readability.

SQL Question 10: Could you describe a self-join and provide a scenario in which it would be used?

A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the FROM clause, and give each instance of the table a different alias. You can then join the two instances of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

Self-joins are the go-to technique for any data analysis that involves pairs of the same thing, like identifying pairs of products that are frequently purchased together like in this Walmart SQL interview question.

For another example, say you were doing an HR analytics project and needed to analyze how much all Mutual of Omaha employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of Mutual of Omaha employees who work in the same department:

This query returns all pairs of Mutual of Omaha employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Mutual of Omaha employee being paired with themselves).

Preparing For The Mutual of Omaha SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Mutual of Omaha SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Mutual of Omaha SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).

Each problem on DataLemur has hints to guide you, detailed solutions and best of all, there is an interactive coding environment so you can right online code up your query and have it checked.

To prep for the Mutual of Omaha SQL interview it is also helpful to solve SQL questions from other insurance companies like:

• Alleghany SQL questions
• Unum Group SQL questions
• Pacific Life SQL questions
• Farmers Insurance SQL questions

Explore the latest news and insights from Mutual of Omaha and discover how they're helping people achieve their financial goals!

In case your SQL query skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this interactive SQL tutorial.

This tutorial covers SQL topics like INTERCEPT/EXCEPT and LAG window function – both of these show up frequently in Mutual of Omaha SQL assessments.

Mutual of Omaha Insurance Data Science Interview Tips

What Do Mutual of Omaha Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems to prepare for the Mutual of Omaha Data Science Interview are:

• AB Testing Questions
• Python or R Coding Questions
• Product & Business Analytics Questions
• ML Modelling Questions
• Behavioral Interview Questions based on Mutual of Omaha values & principles

How To Prepare for Mutual of Omaha Data Science Interviews?

To prepare for Mutual of Omaha Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from companies like Google, Tesla, & Goldman Sachs
• a crash course on Python, SQL & ML
• over 1000+ reviews on Amazon & 4.5-star rating

Don't forget about the behavioral interview – prepare for it with this list of common Data Scientist behavioral interview questions.