11 The Hartford SQL Interview Questions (Updated 2024)
The Hartford employees use SQL for analyzing financial data, including investment portfolio performance and market trends, for risk assessment, as well as creating predictive models for investment strategies, such as identifying high-growth investment opportunities. This is why The Hartford often asks SQL questions in interviews for Data Science and Data Engineering positions.
As such, to help prep you for the The Hartford SQL interview, we've curated 11 Hartford Financial Services Group SQL interview questions – scroll down to start solving them!
11 Hartford Financial Services Group SQL Interview Questions
SQL Question 1: Identify Top Insurance Claim Submitters
For an insurance business like The Hartford, a key activity is the submission of insurance claims. Thus, top customers could be considered those who submit claims frequently. Though this might seem like a bad thing (since the company has to pay out), these customers are actually important because of the revenue they generate through their high premiums.
Say the company keeps a record of every claim submitted in their "claims" database, which has columns: claim_id (the unique ID of the claim), customer_id (the unique ID of the customer submitting the claim), claim_date (the date the claim was submitted), and claim_amount (the monetary amount of the claim).
The SQL interview question could be to "Write a PostgreSQL query to identify the top 10 customers who have submitted the most claims in the past year. Also, provide the total amount claimed by these customers in the same period."
Example Input:
| claim_id | customer_id | claim_date | claim_amount |
|---|---|---|---|
| 9871 | 123 | 06/08/2022 00:00:00 | 4000 |
| 8123 | 265 | 06/10/2022 00:00:00 | 3500 |
| 6590 | 123 | 06/22/2022 00:00:00 | 5000 |
| 9234 | 264 | 07/26/2022 00:00:00 | 4000 |
| 7845 | 123 | 07/27/2022 00:00:00 | 6000 |
Answer:
This query first filters the claims submitted in the past year, then it groups the remaining records by customer_id. For each group, it counts the number of records and sums the claim_amount, which indicates the total number and amount of claims submitted by each customer in the past year. It then orders the customers in descending order of the number of claims they submitted and finally, limits the output to the top 10 customers.
To work on a similar customer analytics SQL question where you can code right in the browser and have your SQL query instantly graded, try this Walmart SQL Interview Question:
SQL Question 2: Highly-Paid Employees
Imagine there was a table of The Hartford employee salaries. Write a SQL query to find the employees who earn more than their own manager.
The Hartford Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
You can solve this interview question and run your code right in DataLemur's online SQL environment:
Answer:
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 hard to understand, you can find a detailed solution here: Employees Earning More Than Their Boss.
SQL Question 3: Can you give some examples of when denormalization might be a good idea?
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting The Hartford's database to ever-changing business needs.
SQL Question 4: Claims Analysis over Time
Given the table, write a SQL query to find the total claim cost and average claim cost for each ‘Business Segment’ (i.e., Commercial, Personal, etc.) for each year. The difference in total claim cost from the previous year would also be part of the results.
Consider the table is structured as follows:
Example Input:
| claim_id | submit_date | business_segment | claim_cost(in USD) |
|---|---|---|---|
| 5142 | 01/10/2017 | Personal | 1500 |
| 3856 | 02/25/2017 | Commercial | 3000 |
| 7629 | 03/15/2017 | Commercial | 2000 |
| 7263 | 05/20/2018 | Personal | 2500 |
| 5419 | 12/30/2018 | Personal | 3000 |
| 9801 | 04/10/2019 | Commercial | 5000 |
Example Output:
| year | business_segment | total_claim_cost | avg_claim_cost | difference_previous_year |
|---|---|---|---|---|
| 2017 | Personal | 1500 | 1500 | N/A |
| 2017 | Commercial | 5000 | 2500 | N/A |
| 2018 | Personal | 5500 | 2750 | 4000 |
| 2019 | Commercial | 5000 | 5000 | 0 |
Answer:
This query calculates the total and average claim cost per year, per business segment. It also uses a window function with an order to find the difference in the total claim cost from the previous year. The function is utilized to find the value of the previous year. If there is no previous year (as in the first row), the is utilized as a default value.
To solve another window function question on DataLemur's free online SQL coding environment, solve this Amazon BI Engineer interview question:
SQL Question 5: What does and do?
Both the and window functions are used to access a row at a specific offset from the current row.
However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.
Say you had a table of salary data for The Hartford employees:
| name | salary |
|---|---|
| Amanda | 130000 |
| Brandon | 90000 |
| Carlita | 80000 |
You could use the function to output the salary of each employee, along with the next highest-paid employee:
This would yield the following output:
| name | salary | next_salary |
|---|---|---|
| Amanda | 130000 | 90000 |
| Brandon | 90000 | 80000 |
| Carlita | 80000 | NULL |
Swapping for would get you the salary of the person who made just more than you:
| name | salary | next_salary |
|---|---|---|
| Amanda | 130000 | NULL |
| Brandon | 90000 | 130000 |
| Carlita | 80000 | 90000 |
SQL Question 6: Calculate Average Insurance Claim per Customer
The Hartford is an insurance company that primarily provides homeowners and auto insurance. As a data analyst for the company, one pertinent task you may have is to be able to calculate the average insurance claim per customer over a certain period of time.
This question will require the use of two tables: and .
The table includes the customer id, name, and signup date. The table includes the claim id, customer id, claim amount, and date of claim submission.
Example Input:
| customer_id | name | signup_date |
|---|---|---|
| 111 | Tom Smith | 01/02/2021 |
| 222 | Jane Doe | 03/15/2021 |
| 333 | John Lee | 05/22/2021 |
| 444 | Ada Byron | 07/08/2021 |
| 555 | Ethan Hall | 09/29/2021 |
Example Input:
| claim_id | customer_id | claim_amount | claim_date |
|---|---|---|---|
| 9001 | 111 | 1000.00 | 02/01/2022 |
| 9002 | 111 | 2500.00 | 04/12/2022 |
| 9003 | 222 | 1500.00 | 05/05/2022 |
| 9004 | 222 | 1200.00 | 06/06/2022 |
| 9005 | 333 | 3000.00 | 06/18/2022 |
Example Output:
| customer_name | avg_claim_amount |
|---|---|
| Tom Smith | 1750.00 |
| Jane Doe | 1350.00 |
| John Lee | 3000.00 |
Answer:
This query first joins the table (alias c) with the table (alias cl) using the field. With this joined table, it then calculates the average claim amount () for each customer (). This amount is returned alongside the name of each customer.
SQL Question 7: What's the purpose of a foreign key?
A foreign key is a column or group of columns in a table that refers to the primary key in another table. The foreign key constraint helps maintain referential integrity between the two tables.
Let's examine employee data from The Hartford's HR database:
:
| employee_id | first_name | last_name | manager_id |
|---|---|---|---|
| 1 | Aubrey | Graham | 3 |
| 2 | Marshal | Mathers | 3 |
| 3 | Dwayne | Carter | 4 |
| 4 | Shawn | Carter |
In this table, serves as the primary key and functions as a foreign key because it links to the of the employee's manager. This establishes a relationship between The Hartford employees and their managers, allowing for easy querying to find an employee's manager or see which employees report to a specific manager.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to connect each employee to their respective department and location.
SQL Question 8: Customer Policy Details
Given the customer policy database for The Hartford, filter for customers that have "homeowners" policy and reside in "Hartford, Connecticut". Respond with their customer id, policy number, policy start date, and policy end date.
Let's consider the following tables representing the relevant customer, policy, and address information:
Example Input:
| customer_id | first_name | last_name | address_id |
|---|---|---|---|
| 1 | John | Doe | 101 |
| 2 | Jane | Smith | 102 |
| 3 | Harry | Potter | 103 |
| 4 | Ron | Weasley | 104 |
| 5 | Hermione | Granger | 105 |
Example Input:
| policy_number | policy_type | customer_id | policy_start_date | policy_end_date |
|---|---|---|---|---|
| 001 | Homeowners | 1 | 2020/10/01 | 2021/10/01 |
| 002 | Auto | 2 | 2020/05/01 | 2021/05/01 |
| 003 | Homeowners | 4 | 2020/04/01 | 2021/04/01 |
| 004 | Homeowners | 3 | 2020/06/01 | 2021/06/01 |
| 005 | Auto | 5 | 2020/05/05 | 2021/05/05 |
Example Input:
| address_id | city | state |
|---|---|---|
| 101 | Hartford | Connecticut |
| 102 | New Haven | Connecticut |
| 103 | Hartford | Connecticut |
| 104 | Stamford | Connecticut |
| 105 | Bridgeport | Connecticut |
Answer:
For solving this problem, we'll join the three tables based on their relationships and then use the WHERE clause to filter.
This solution joins , , and tables together and filters for 'homeowners' policies that also have an address in 'Hartford, Connecticut'. The result will be a list of customer ids, their policy numbers, policy start dates, and end dates.
SQL Question 9: Average Claim Amount per Policy Type for The Hartford
You are a data analyst at The Hartford, an insurance company. For a particular year, the management wants to know the average amount claimed by customers for each type of insurance policy. Write a SQL query to generate a report showing the policy type and the average claim amount sorted by the average claim amount in descending order.
Example Input:
| policy_id | customer_id | policy_type |
|---|---|---|
| P001 | C001 | Car |
| P002 | C002 | Home |
| P003 | C003 | Life |
| P004 | C004 | Home |
| P005 | C001 | Life |
Example Input:
| claim_id | policy_id | claim_amount |
|---|---|---|
| C101 | P001 | 500 |
| C102 | P002 | 1000 |
| C103 | P003 | 10000 |
| C104 | P004 | 1500 |
| C105 | P001 | 700 |
Example Output:
| policy_type | avg_claim_amount |
|---|---|
| Life | 10000.00 |
| Home | 1250.00 |
| Car | 600.00 |
Answer:
Explanation: In this query, we are joining the table with the table on the column, as it is common to both tables. We then group the joined data by the , and for each group, we compute the average claim amount. The AVG function in PostgreSQL automatically calculates the average of a group of values. Finally, we sort the result by the average claim amount in a descending order to get the policy types with the highest average claim amount at the top.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring an aggregation query on grouped data or this Amazon Average Review Ratings Question which is similar for requiring average calculations grouped by a certain category.
SQL Question 10: How can you determine which records in one table are not present in another?
To discover records in one table that are not present in another, you can utilize a and filter out any values in the right-side table.
For example, say you had a table of The Hartford customers and a 2nd table of all purchases made with The Hartford. To find all customers who did not make a purchase, you'd use the following
This query returns all rows from the customers table, along with any matching rows from the purchases table. If there is no matching row in the purchases table, values will be returned for all of the right table's columns. The clause then filters out any rows where the purchases.id column is , leaving only customers who have not made a purchase.
SQL Question 11: Calculate Monthly Premium Income
As a Data Analyst for The Hartford, a leading provider of insurance services, you are asked to find out monthly premium income of different insurance services provided by the company. The table has a row for each premium payment from a customer with the amount paid (), the , and the date the payment was made (). Each unique represents a specific insurance policy sold by the company.
Example Input:
| payment_id | policy_number | payment_date | premium_rate |
|---|---|---|---|
| 1001 | INS301 | 2022-07-01 | 300 |
| 1002 | INS102 | 2022-07-02 | 500 |
| 1003 | INS905 | 2022-07-02 | 200 |
| 1004 | INS102 | 2022-08-01 | 500 |
| 1005 | INS301 | 2022-08-02 | 300 |
| 1006 | INS905 | 2022-08-03 | 200 |
| 1007 | INS905 | 2022-09-02 | 200 |
| 1008 | INS301 | 2022-09-03 | 300 |
| 1009 | INS102 | 2022-09-03 | 500 |
Your task is to create a SQL query that will showcase the total premium collected () each month for each insurance policy.
Answer:
This query first extracts the month from for each row and then groups by the result and the policy number. It then sums up the for each group (representing the total premium income for each insurance policy each month) and orders the result first by month and then by total premium income, in descending order.
Example Output:
| payment_month | policy_number | total_premium_collected |
|---|---|---|
| 7 | INS102 | 500 |
| 7 | INS301 | 300 |
| 7 | INS905 | 200 |
| 8 | INS102 | 500 |
| 8 | INS301 | 300 |
| 8 | INS905 | 200 |
| 9 | INS102 | 500 |
| 9 | INS301 | 300 |
| 9 | INS905 | 200 |
Preparing For The The Hartford SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the The Hartford SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the above The Hartford SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Microsoft, Google, and Meta.
Each interview question has hints to guide you, full answers and crucially, there's an interactive SQL code editor so you can instantly run your SQL query and have it graded.
To prep for the The Hartford SQL interview it is also helpful to practice interview questions from other insurance companies like:
- Travelers SQL questions
- MassMutual SQL questions
- Northwestern Mutual SQL questions
- USAA SQL questions
Learn how Hartford is helping navigate the impact of AI on labor and employment!
In case your SQL coding skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this SQL interview tutorial.
This tutorial covers SQL concepts such as aggregate functions like SUM()/COUNT()/AVG() and LEAD/LAG window functions – both of which show up frequently during The Hartford interviews.
Hartford Financial Services Group Data Science Interview Tips
What Do The Hartford Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories to practice for the The Hartford Data Science Interview include:
- Stats Interview Questions
- Python or R Coding Questions
- Product Analytics Questions
- ML Modelling Questions
- Behavioral Questions centered on The Hartford cultural values
How To Prepare for The Hartford Data Science Interviews?
To prepare for The Hartford Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from companies like Google, Tesla, & Goldman Sachs
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Don't forget about the behavioral interview – prepare for that using this list of common Data Scientist behavioral interview questions.