# 9 Brighthouse Financial SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

Brighthouse Financial employees use SQL for analyzing and managing vast financial data sets, including customer demographics and policy details, as well as building and optimizing the performance of insurance policy and investment portfolio databases, such as optimizing database indexing for faster query execution. This is why Brighthouse Financial uses SQL questions during interviews for Data Science, Data Engineering and Data Analytics jobs.

To help you practice for the Brighthouse Financial SQL interview, here’s 9 Brighthouse Financial SQL interview questions in this blog.

## 9 Brighthouse Financial SQL Interview Questions

### SQL Question 1: Analyzing policy premiums

As an insurance company, one of the important metrics that Brighthouse Financial tracks is the trend of average policy premiums for each of its policy types. Premiums can vary over the months. To understand the trend over the past year, you're asked to write a SQL query that calculates the average monthly premium for each insurance policy type.

Given a table, where each row represents a policy sold by the company.

##### Example Input:
101Life Insurance2022-01-142000
102Annuity2022-01-201500
103Life Insurance2022-02-102200
104Disability Income2022-02-151700
105Annuity2022-03-051600

The company needs a report:

##### Example Output:
1Life Insurance2000
1Annuity1500
2Life Insurance2200
2Disability Income1700
3Annuity1600

Here the column represents the month part of the , and is the average premium of that policy type in that particular month.

In the query above, we extract the month part from the using the function. We then use to calculate the average premium per policy type, grouping by the month of and . The clause is used to sort the result by month, then by policy type.

For more window function practice, try this Uber SQL Interview Question on DataLemur's online SQL code editor:

### SQL Question 2: Employees Earning More Than Their Boss

Given a table of Brighthouse Financial employee salary data, write a SQL query to find employees who earn more money than their own manager.

#### Brighthouse Financial Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

#### Example Output:

employee_idemployee_name
3Olivia 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:

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 confusing, you can find a step-by-step solution here: Employee Salaries Higher Than Their Manager.

### SQL Question 3: How would you improve the performance of a slow SQL query?

Here's some strategies that can generally speed up a slow SQL query:

• Only query the columns you actually need
• Index the columns used in the query
• Use the right kind of JOIN (be explicit about inner vs. outer joins)
• Use the right data types (for example INT vs BIGINT can save you disk space and memory)

While there's an entire industry dedicated to database performance tuning , for Data Analyst and Data Science interviews at Brighthouse Financial, knowing the above basics should be more than enough to ace the SQL interview! Data Engineers on the other hand should know more about the command and understand a bit more about a query-execution plan.

### SQL Question 4: Calculate Average Policy Premiums

As an employee of Brighthouse Financial, you are tasked to find the average policy premium for each policy type in the company. Each policy has its own premium and has a different policy type. The main question is: What is the average policy premium based on policy type?

The table for this question might look like the following:

##### Example Input:
1013456Life Insurance1500
1024567Annuity2000
1033456Life Insurance1700
1048910Annuity2100
1053456Life Insurance1600

In order to solve this problem, we need to use the function in conjunction with the clause since we want to find the average for each type of policy. Here is an example of how to perform this operation:

The above SQL query starts by selecting the column and the average of the column from the table. The clause is used to divide the into groups, each of which have the same value for . The function is then applied to each group separately.

##### Example Output:
Life Insurance1600
Annuity2050

This result implies that the average policy premium for 'Life Insurance' is 1600, while for 'Annuity', it is 2050.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for conducting average calculations on a dataset or this Alibaba Compressed Mean Question which is similar for performing analysis on grouped data.

### SQL Question 5: What's a primary key?

A primary key is a column or group of columns that uniquely identifies a row in a table. For example, say you had a database of Brighthouse Financial marketing campaigns data:

In this Brighthouse Financial example, the CampaignID column is the primary key of the MarketingCampaigns table. The constraint ensures that no two rows have the same CampaignID. This helps to maintain the integrity of the data in the table by preventing duplicate rows.

### SQL Question 6: Filter Customer Records

You are given a customer records database for Brighthouse Financial. The task is to write an SQL query to find all customer records that have an email address ending with '@brighthouse.com'.

Assume the table has the following schema:

#### Example Input:

customer_idfirst_namelast_nameemail
3932JohnDoejohn.doe@brighthouse.com
5876EmilyJohnsonemily.johnson@yahoo.com
8162MichaelRossmichael.ross@brighthouse.com
9641EmmaWilliamsemma.williams@gmail.com

Your query should return only the records of customers with '@brighthouse.com' in their email.

#### Example Output:

customer_idfirst_namelast_nameemail
3932JohnDoejohn.doe@brighthouse.com
8162MichaelRossmichael.ross@brighthouse.com

The SQL keyword is used in the clause to search for a specific pattern in a column. The '%' symbol is a wildcard character that represents zero or more characters. In this case, the query is filtering the 'email' column to return only those rows where the email address ends with '@brighthouse.com'.

### SQL Question 7: Can you explain the distinction between an inner and a full outer join?

An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.

For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Brighthouse Financial sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.

: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.

: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.

### SQL Question 8: Analyze Customer and Policy Information

For a financial company like Brighthouse Financial, the data is usually centered around their core business - insurance policies. Let's create an SQL exercise that analyzes customer and their policies.

Brighthouse Financial offers life insurance products to its customers. Two important tables in their database may be and table. You are tasked to write a SQL query that returns the number of insurance policies bought by customers in each of the states.

##### Example Input:
customer_idfirst_namelast_namestate
1JohnDoeCalifornia
2JaneDoeFlorida
3PaulSmithTexas
4AliceJohnsonCalifornia
##### Example Input:
policy_idcustomer_idpolicy_typestatus
1011Term LifeActive
1022Universal LifeActive
1033Term LifeLapsed
1041Term LifeActive
1054Term LifeActive

This SQL query joins the table with the table on the field. It filters for only active policies in the table. It then groups the results by the field in the table and counts the number of policies in each state.

##### Example Output:
statenumber_of_policies
California3
Florida1

This result tells us that there are 3 active policies belonging to customers from California, and 1 active policy belonging to a customer from Florida.

Since joins come up frequently during SQL interviews, practice this Snapchat JOIN SQL interview question:

### SQL Question 9: Calculate the Standard Deviation of Customer Deposits

Brighthouse Financial wants to analyze the variability in deposits made by customers into their accounts on a monthly basis. You will need to calculate the mean, variance, and standard deviation for each customer's monthly deposit and round these values.

For accounts that have no deposits in a month, treat them as zero; ignore accounts with no deposits in the entire period.

Consider the ‘deposits’ table with the following schema:

##### Example Input:
deposit_idcustomer_idmonthyeardeposit_amount
1011012022500.0
1021022022600.0
10320120221000.0
10420320221500.0
1053022022300.0
1063032022200.0
1073042022150.0

Compute the mean, variance, and standard deviation (sqrt of variance) of the monthly deposits per customer and their respective rounded values.

##### Example Output:
customer_idmean_depositround_mean_depositvariance_depositround_variance_depositstd_dev_depositround_std_dev_deposit
1550.005505000.00500070.7171
21250.00125062500.0062500250.00250
3215.002155050.00505071.0771

The above SQL query performs calculations at two different levels to compute the mean, variance, and standard deviation. First, it calculates the mean deposits at a customer level by grouping by . Then, it goes on to compute the variance, again at a customer level. Finally, by joining the two CTEs (MeanDeposits and VarianceDeposits), it provides the required output. The sqrt function is used to compute the standard deviation. The ROUND function is used to round off the results.

To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for calculations including mean and rounding or this Alibaba Compressed Mode Question which is similar for analyzing transactional data.

### Preparing For The Brighthouse Financial SQL Interview

The key to acing a Brighthouse Financial SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier Brighthouse Financial SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Google, Facebook, Microsoft and Amazon.

Each exercise has hints to guide you, detailed solutions and best of all, there is an interactive coding environment so you can right in the browser run your SQL query and have it checked.

To prep for the Brighthouse Financial SQL interview you can also be a great idea to solve SQL problems from other insurance companies like:

Get the scoop on Brighthouse Financial's latest initiatives and innovations in the world of insurance and finance!

In case your SQL coding skills are weak, forget about going right into solving questions – go learn SQL with this interactive SQL tutorial.

This tutorial covers SQL topics like filtering on multiple conditions using AND/OR/NOT and INTERCEPT/EXCEPT – both of these come up frequently in Brighthouse Financial SQL interviews.

### Brighthouse Financial Data Science Interview Tips

#### What Do Brighthouse Financial Data Science Interviews Cover?

In addition to SQL interview questions, the other topics to prepare for the Brighthouse Financial Data Science Interview include:

#### How To Prepare for Brighthouse Financial Data Science Interviews?

To prepare for Brighthouse Financial Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from FAANG tech companies
• a refresher covering Python, SQL & ML
• over 1000+ reviews on Amazon & 4.5-star rating

Don't forget about the behavioral interview – prep for it with this Behavioral Interview Guide for Data Scientists.