8 MetLife SQL Interview Questions (Updated 2024)
MetLife employees write SQL queries for analyzing customer policy details, such as policy coverage and claims history, as well as for generating business intelligence reports, including sales performance and market trend analysis, to support strategic decision making. That is why MetLife asks SQL problems in interviews for Data Science, Data Engineering and Data Analytics jobs.
So, to help you prepare, we've curated 8 MetLife SQL interview questions – able to solve them?
8 MetLife SQL Interview Questions
SQL Question 1: Find Top Life Insurance Policyholder
As a data analyst for MetLife, a leading insurance company, you are tasked to identify the so-called "whale users"– these are customers who have bought a large number of life insurance policies from your company. Specifically, write a SQL query to analyze the insurance policyholder database, and find the customers who have bought the most number of life insurance policies.
Example Input:
| policy_id | user_id | purchase_date | policy_name | policy_amount |
|---|---|---|---|---|
| 1001 | 101 | 12/10/2020 | Term Insurance | 100,000 |
| 1002 | 102 | 01/05/2021 | Whole Life Insurance | 150,000 |
| 1003 | 101 | 03/30/2021 | Universal Life Insurance | 125,000 |
| 1004 | 103 | 04/22/2021 | Variable Life Insurance | 200,000 |
| 1005 | 101 | 06/15/2021 | Term Insurance | 75,000 |
Example Input:
| user_id | user_name | user_city |
|---|---|---|
| 101 | John Doe | New York |
| 102 | Jane Smith | Los Angeles |
| 103 | Robert Johnson | Chicago |
Answer:
The above SQL query joins and tables on , groups the records by and , and counts the number of policies each customer has purchased. It then orders the results in descending order by the policy count, which effectively lists the customers with the most policies at the top. The statement is used to limit the results to the top 5 customers.
To solve a related customer analytics question on DataLemur's free online SQL coding environment, try this Microsoft Azure Cloud SQL Interview Question:
SQL Question 2: Top Three Salaries
Assume there was a table of MetLife employee salary data. Write a SQL query to find the top three highest paid employees in each department.
MetLife Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Test your SQL query for this question directly within the browser 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 code above is hard to understand, you can find a step-by-step solution here: Top 3 Department Salaries.
SQL Question 3: What is a primary key?
The primary key of a table is a column or combination of columns that serves to uniquely identify each row in the table. To define a primary key in a SQL database, you can use the constraint.
For instance, consider a table of :
In this example, the column is the primary key of the MetLife employees table.
Primary keys are important in databases for several reasons:
- Uniqueness: A primary key is used to uniquely identify each row in a table. This means that no two rows in the table can have the same primary key value. This is important because it helps to ensure the accuracy and integrity of the data in the table.
- Non-nullability: A primary key is typically defined as a non-null column, which means that it cannot contain a null value. This helps to ensure that every row in the table has a unique identifier.
- Relationship-building: Primary keys are often used to establish relationships between tables in a database. For example, you might use a primary key to link the table to the table.
SQL Question 4: Determine Average Premium by City and Year
MetLife, being an insurance company, may be interested in analyzing their policies data for business insights. For example, they may want to calculate the average insurance premium for each city they operate in, grouped by each year.
Given the following table, write a PostgreSQL query to determine the average insurance premium for each city and year.
Example Input:
| policy_id | holder_id | issue_date | expiry_date | city | annual_premium |
|---|---|---|---|---|---|
| 8902 | 123 | 01/07/2020 | 01/07/2021 | New York | 1200 |
| 8701 | 265 | 12/12/2020 | 12/12/2021 | Chicago | 980 |
| 9021 | 362 | 03/18/2021 | 03/18/2022 | Boston | 1100 |
| 8642 | 192 | 07/26/2021 | 07/26/2022 | New York | 1300 |
| 7822 | 981 | 01/05/2020 | 01/05/2021 | Boston | 1050 |
Answer:
This PostgreSQL query makes use of the Extraction function combined with the AVG aggregate function in Window mode. This will give the average annual premium for each city per year. The WINDOW function PARTITION BY is used to divide the result set into partitions (based on city and year) and AVG is applied to each of these partitions. Ordering by year and city ensures that the results are presented in a logical and readable way.
p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: What does the SQL keyword do?
"The clause in SQL allows you to select records that are unique, eliminating duplicates.
For a tangible example, say you had a table of MetLife employees:
| first_name | job_title |
|---|---|
| Akash | Data Analyst |
| Brittany | Data Scientist |
| Carlos | Data Engineer |
| Diego | Data Engineer |
| Eva | Data Analyst |
If you were doing an HR Analytics project and you wanted to get all the unique job titles that currently worked at the company, you would write the following SQL query:
The output would give you 3 distinct job titles at MetLife:
| job_title |
|---|
| Data Analyst |
| Data Scientist |
| Data Engineer |
SQL Question 6: Calculate the Average Insurance Claim Amount for Each Policy Type
MetLife, a major insurance company, would like to understand how much an average claim costs them per policy type. This will help them in risk assessment and pricing their policies. Write a SQL query that calculates the average claim amount for each insurance policy type.
Example Input:
| policy_id | policy_type | customer_id |
|---|---|---|
| 001 | Auto Insurance | Cust001 |
| 002 | Home Insurance | Cust002 |
| 003 | Life Insurance | Cust003 |
| 004 | Auto Insurance | Cust004 |
| 005 | Life Insurance | Cust005 |
Example Input:
| claim_id | policy_id | claim_amount |
|---|---|---|
| 301 | 001 | 500 |
| 302 | 002 | 1000 |
| 303 | 003 | 2000 |
| 304 | 001 | 600 |
| 305 | 002 | 1200 |
| 306 | 003 | 2500 |
| 307 | 004 | 700 |
| 308 | 005 | 3000 |
Answer:
The above query will return the average claim amount for each type of insurance policy. It works by first joining the policy table with the claims table on the policy_id. Then it groups the results by policy_type and calculates the average claim amount for each group. This allows us to see the average claim amount per insurance policy type.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring aggregations on categories or this JPMorgan Chase Cards Issued Difference Question which is similar for performing calculations on grouped data.
SQL Question 7: What is the purpose of the constraint, and when might it be helpful to use it?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
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.
SQL Question 8: Analyze Customers and Policies
Assume that you are asked by MetLife, a leading provider of insurance, annuities, and employee benefit programs, to perform an analysis on the demographics of their customers and their associated policies. MetLife wants to understand which type of policy has the highest total premium by state and age group.
You are given access to two tables, and , shown below:
Example Input:
| customer_id | first_name | last_name | dob | state |
|---|---|---|---|---|
| 1 | John | Doe | 1975-05-14 | NY |
| 2 | Jane | Smith | 1985-10-23 | NY |
| 3 | Bob | Johnson | 1967-03-02 | CA |
| 4 | Alice | Williams | 1990-12-07 | CA |
| 5 | Charlie | Brown | 1980-11-16 | NJ |
Example Input:
| policy_id | customer_id | policy_type | premium |
|---|---|---|---|
| 1001 | 1 | Auto | 1500 |
| 1002 | 2 | Life | 1200 |
| 1003 | 3 | Health | 1300 |
| 1004 | 4 | Auto | 1600 |
| 1005 | 5 | Life | 1100 |
Write a SQL query in PostgreSQL to merge these two tables and display the total policy premium by state and age group (under 40, 40 to 60, and over 60), only for the policy type with the highest total premium in each state.
Answer:
This query first determines the age groups of the customers using the , , and functions in PostgreSQL. It then calculates the total premium for each policy type per state. After finding the policy type with the highest total premium in each state, the query joins the datasets to yield the final output.
Because joins come up frequently during SQL interviews, practice an interactive SQL join question from Spotify:
MetLife SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the MetLife SQL interview is to solve as many practice SQL interview questions as you can!
Besides solving the earlier MetLife SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Microsoft and Silicon Valley startups.
Each SQL question has multiple hints, full answers and most importantly, there is an online SQL coding environment so you can right in the browser run your SQL query answer and have it graded.
To prep for the MetLife SQL interview it is also helpful to solve interview questions from other insurance companies like:
- Citigroup SQL questions
- State Farm SQL questions
- Wells Fargo SQL questions
- Bank of America SQL questions
Discover how MetLife is using AI to enhance the customer experience and make a real difference in people's lives!
In case your SQL foundations are weak, forget about diving straight into solving questions – go learn SQL with this interactive SQL tutorial.
This tutorial covers things like UNION and filtering with LIKE – both of which pop up routinely in MetLife SQL interviews.
MetLife Data Science Interview Tips
What Do MetLife Data Science Interviews Cover?
Beyond writing SQL queries, the other topics tested in the MetLife Data Science Interview include:
- AB Testing & Experimentation Questions
- Coding Questions in Python or R
- Data Case Study Questions
- ML Modelling Questions
- Behavioral Interview Questions based on MetLife company values
How To Prepare for MetLife Data Science Interviews?
To prepare for MetLife Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Google, Microsoft & tech startups
- a refresher covering SQL, Product-Sense & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Also focus on the behavioral interview – prep for it using this Behavioral Interview Guide for Data Scientists.