11 Erie Insurance SQL Interview Questions (Updated 2024)
At Erie Insurance Group, SQL is used for analyzing policy holder data, including claims history and demographic information, to assess risk factors, as well as managing and retrieving customer data to personalize insurance service offerings, like tailoring policy recommendations. This is why Erie Insurance often asks SQL questions during interviews for Data Analyst, Data Science, and BI jobs.
So, to help you prepare, here's 11 Erie Insurance Group SQL interview questions – can you solve them?
11 Erie Insurance Group SQL Interview Questions
SQL Question 1: Identify Power Users for Erie Insurance
As a data analyst for Erie Insurance, your task is to identify the power users. These users are considered vital to the business as they make frequent and high-value insurance purchases. Write a SQL query that identifies users who have made more than 10 purchases and where the total value of these purchases is greater than $10,000 in the last 12 months.
Example Input:
| user_id | name |
|---|---|
| 1 | John |
| 2 | Sara |
| 3 | Alan |
Example Input:
| policy_id | user_id | purchase_date | value |
|---|---|---|---|
| 101 | 1 | 06/08/2020 | 5000 |
| 102 | 1 | 08/10/2020 | 6000 |
| 103 | 1 | 03/10/2021 | 5500 |
| 104 | 2 | 07/05/2021 | 7000 |
| 105 | 3 | 06/06/2021 | 6000 |
| 106 | 3 | 06/18/2021 | 4000 |
Answer:
This Postgresql query starts by selecting the user_id from the policies table where the purchase_date is within the last year. It uses the HAVING clause to filter for users who have purchased more than 10 policies and the total value of the policies is over $10,000. This result is then joined with the users table to get the names of the power users.
To work on another SQL customer analytics question where you can solve it right in the browser and have your SQL solution automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Top Department Salaries
Assume you had a table of Erie Insurance employee salary data. Write a SQL query to find the top 3 highest paid employees in each department.
Erie Insurance 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 |
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 code above is hard to understand, you can find a step-by-step solution with hints here: Top 3 Department Salaries.
SQL Question 3: What is normalization?
Database normalization is the process of breaking down a table into smaller and more specific tables and defining relationships between them via foreign keys. This minimizes redundancy, and creates a database that's more flexible, scalable, and easier to maintain. It also helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies.
SQL Question 4: Calculate Monthly Average Insurance Claim Amount
Erie Insurance needs to monitor the insurance claims made by customers on a monthly basis. The company wants you to write a SQL query to calculate the average claim amount per type of insurance policy each month. The data is stored in the 'claims' table. You need to use a SQL window function to solve this problem.
Here is the data stored in the 'claims' table.
Example Input:
| claim_id | policy_type | claim_date | claim_amount |
|---|---|---|---|
| 9171 | Auto | 01/06/2022 | 2000.00 |
| 8272 | Home | 02/08/2022 | 3560.00 |
| 4393 | Auto | 01/12/2022 | 1850.00 |
| 7472 | Life | 03/22/2022 | 5000.00 |
| 4517 | Life | 03/15/2022 | 7000.00 |
The output should include the month, policy type, and the average claim amount for that policy in that month. Round the amount to 2 decimal places.
Answer:
In the above query, we extract the month from the claim date and partition the data by the month and policy type. The window function is then applied over each partition to calculate the average claim amount per policy each month. The statement sorts the result by month and policy type.
To solve a related window function SQL problem on DataLemur's free online SQL coding environment, solve this Google SQL Interview Question:
SQL Question 5: How would you speed up a slow SQL query?
First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. You might discover that your query is inefficient, or that there are many database writes at the same time you are doing a read, or maybe too many people are concurrently running queries on the same database server.
For Data Analyst and Data Science positions, knowing the ins-and-outs of SQL performance tuning is out-of-scope for the SQL interview round. However, knowing that joins are expensive, and indexes can speed up queries, is generally enough of an answer for Erie Insurance SQL interviews.
SQL Question 6: Auto Insurance Premium Calculation
Scenario: At Erie Insurance, you are tasked with figuring out the monthly premium rates for different classes of auto insurance policyholders. The factors used for calculating the premiums include the age of the driver, their driving experience, and the number of road incidents they've had. The data for these factors is stored in the below tables:
Input:
| policyholder_id | name | birthdate |
|---|---|---|
| 1 | John Doe | 01/14/1980 |
| 2 | Jane Doe | 08/24/1985 |
| 3 | Rob Smith | 09/12/1992 |
Input:
| incident_id | policyholder_id | incident_date | severity |
|---|---|---|---|
| 101 | 1 | 05/05/2022 | Minor |
| 102 | 1 | 11/12/2021 | Major |
| 103 | 2 | 06/25/2022 | Minor |
| 104 | 2 | 03/10/2022 | Minor |
| 105 | 3 | 04/19/2022 | Major |
| 106 | 3 | 02/23/2022 | Major |
Input:
| policyholder_id | driving_experience_years |
|---|---|
| 1 | 12 |
| 2 | 8 |
| 3 | 4 |
Write a SQL query to calculate the car insurance premium for each policyholder, based on the following rules:
- Start with a base rate of $500.
- For each Major incident, increase the rate by $100.
- For each Minor incident, increase the rate by $50.
- For each year of driving experience, decrease the rate by $20, but not more than the base rate.
Answer:
The PostgreSQL query for this would be as follows:
The provided query calculates the insurance premium based on the criteria outlined. For each policyholder, It starts with a base rate of $500, then adds extra charges for registered incidents based on their severity, and finally subtracts discounts based on the driver's years of experience. If a policyholder has not had any incidents, the coalesce function will treat the sum of penalties as 0. Also, the discount for driving experience will not exceed the base rate, as safeguarded by the LEAST function.
SQL Question 7: What does the SQL command do?
is used to combine the output of multiple statements into one big result!
For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Erie Insurance, and data on potential sales leads lived in both Salesforce CRM and Hubspot. To write a query to analyze leads created after 2023 started, across both CRMs, you could use in the following way:
SQL Question 8: Average Claim Amount by Insurance Category
In Erie Insurance, we offer many types of insurance policies, for example, auto, home, life, etc. We receive different claims against these policies. We are interested in finding the average claim amount per insurance category. Please write a SQL query to find the average claim amount for each insurance category.
Example Input:
| claim_id | policy_id | claim_date | claim_amount |
|---|---|---|---|
| 1001 | A001 | 01/05/2022 00:00:00 | 3500 |
| 1023 | B045 | 02/03/2022 00:00:00 | 5700 |
| 1145 | A001 | 07/22/2022 00:00:00 | 4600 |
| 1208 | B045 | 04/15/2022 00:00:00 | 6200 |
| 1345 | C079 | 05/20/2022 00:00:00 | 2300 |
Example Input:
| policy_id | category |
|---|---|
| A001 | Auto |
| B045 | Home |
| C079 | Life |
Example Output:
| category | avg_claim_amount |
|---|---|
| Auto | 4050.00 |
| Home | 5950.00 |
| Life | 2300.00 |
Answer:
This SQL query begins by selecting the 'category' column from the 'policies' table and calculates the average claim amount from the 'claims' table. The JOIN clause links 'claims' and 'policies' where the 'policy_id's match. Finally, it groups the results by insurance 'category', which provides the average claim amount for each type of insurance. To achieve the correct decimal format, ensure your database is set to handle decimal places in averages (it generally does).
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating most profitable items per category or this Amazon Average Review Ratings Question which is similar for calculating average values per category.
SQL Question 9: Calculate the Click-Through-Rates for Insurance Quoting
Erie Insurance is a premium insurance provider, and they use digital ads for potential customers to click and get an insurance quote from their website. We want to calculate the click-through-rates (CTR). The metric is defined as the number of potential customers who visited the ad and the number of potential customers who proceeded with getting a quote.
Use the following tables:
Example Input:
| ad_id | user_id | ad_viewed_date |
|---|---|---|
| 291 | 123 | 06/08/2022 00:00:00 |
| 368 | 265 | 06/10/2022 00:00:00 |
| 472 | 362 | 06/18/2022 00:00:00 |
| 529 | 498 | 06/18/2022 00:00:00 |
| 356 | 277 | 07/26/2022 00:00:00 |
Example Input:
| quote_id | user_id | quote_created_date |
|---|---|---|
| 4516 | 123 | 06/08/2022 00:30:00 |
| 6123 | 265 | 06/11/2022 11:12:00 |
| 7835 | 687 | 06/19/2022 20:20:00 |
| 8781 | 748 | 07/15/2022 16:24:00 |
| 4515 | 277 | 07/26/2022 12:05:00 |
Answer:
This query provides the click-through-rates (CTR) on a monthly basis. It calculates the ratio of distinct users who have created a quote after seeing an ad to the total distinct users who have seen the ad, grouped by each month. The dates of ad views and quote creations are truncated to the day level to ensure the user created the quote on the same day they viewed the ad. The output will be the counts of distinct users who viewed the ad and created quotes, and their associated CTR for each month.
To practice another question about calculating rates, try this SQL interview question from TikTok on DataLemur's online SQL code editor:
SQL Question 10: Can you describe the role of the constraint and provide an example of a situation where it might be applied?
The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.The CHECK constraint is often used in conjunction with other constraints, such as NOT NULL or UNIQUE.
You might consider implementing the CHECK constraint in your database if you want to ensure that certain data meets specific conditions. This can be helpful for maintaining the quality and reliability of your data.
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 11: Filter Customer Records Based on Address
As an Erie Insurance Data Analyst, you have been handed a task to retrieve a list of all customer records where the city in their address matches a specific pattern. Write a PostgreSQL query to fetch all customers whose city name in their address begins with 'New'.
Assuming we have a table as follows:
Example Input
| customer_id | first_name | surname | address |
|---|---|---|---|
| 001 | John | Doe | 123 Main St, New York, NY |
| 002 | Jane | Doe | 456 Elm St, Newark, NJ |
| 003 | Jim | Doe | 789 Pine St, Newton, MA |
| 004 | Julie | Doe | 111 Oak St, Newport, RI |
| 005 | Joe | Doe | 333 Cedar St, Erie, PA |
Your task is to find all customers who live in a city starting with 'New'.
Answer:
Here’s the result that you should expect, i.e., only rows where customers' city names start with 'New' should be returned:
Example Output:
| customer_id | first_name | surname | address |
|---|---|---|---|
| 001 | John | Doe | 123 Main St, New York, NY |
| 002 | Jane | Doe | 456 Elm St, Newark, NJ |
| 003 | Jim | Doe | 789 Pine St, Newton, MA |
| 004 | Julie | Doe | 111 Oak St, Newport, RI |
As seen from the output, our PostgreSQL query returns all records where the city name in the address starts with 'New'.
How To Prepare for the Erie Insurance 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 Erie Insurance SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each interview question 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 graded.
To prep for the Erie Insurance SQL interview you can also be wise to practice SQL questions from other insurance companies like:
- Cincinnati Fin SQL questions
- Thrivent SQL questions
- Auto-Owners SQL questions
- Unum Group SQL questions
Explore the latest news and announcements from Erie Insurance and discover how they're innovating in the insurance industry!
But if your SQL skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as Subqueries and aggregate functions like SUM()/COUNT()/AVG() – both of these pop up often during Erie Insurance SQL interviews.
Erie Insurance Group Data Science Interview Tips
What Do Erie Insurance Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems covered in the Erie Insurance Data Science Interview are:
- Statistics and Probability Questions
- Python Pandas or R Coding Questions
- Product-Sense and Business Case Questions
- ML Interview Questions
- Behavioral Questions centered on Erie Insurance cultural values
How To Prepare for Erie Insurance Data Science Interviews?
To prepare for Erie Insurance Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from Google, Microsoft & tech startups
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Don't forget about the behavioral interview – prepare for that using this behavioral interview question bank.