11 Equitable SQL Interview Questions (Updated 2024)

At Equitable Holdings, SQL is used for analyzing financial data for risk management, including stress testing and capital adequacy analysis, as well as optimizing insurance policy algorithms based on customer behavior data, such as policyholder retention rates and customer lifetime value. That is why Equitable often asks SQL problems during interviews for Data Science and Data Engineering positions.

Thus, to help you practice, here’s 11 Equitable Holdings SQL interview questions – able to answer them all?

11 Equitable Holdings SQL Interview Questions

SQL Question 1: Identifying Power Users at Equitable

Equitable is a leading investment company. Their customer database contains information about each users' transactions including the number of transactions and total amount spent. A 'Power User' at Equitable is one who has made more than 100 transactions or spent more than $50000 in the past month. Write a SQL query to identify these power users.

Example Input:

Answer:

This query works by filtering for transactions that took place within the current month using the DATE_TRUNC function and the current system date. Then, it groups the transactions by user_id and uses the HAVING clause to filter for users that have more than 100 transactions or a sum of amount_spent greater than $50000. The result is a list of user_ids representing the power users.

To practice a related super-user data analysis question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:

SQL Question 2: Well Paid Employees

Given a table of Equitable employee salary information, write a SQL query to find employees who make more than their own manager.

Equitable Example Input:

Example Output:

This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.

Try this problem 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 code above is confusing, you can find a detailed solution here: Well Paid Employees.

SQL Question 3: What is database denormalization, and when is it a good idea to consider 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 Equitable. 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 4: Calculate Monthly Average Ratings for Each Product

Equitable carries several products and receives customer reviews often. They like to track the monthly performance of each product based on these reviews. Please write a SQL query that calculates the average star rating for each product on a monthly basis.

We'll use the 'reviews' table, which has the following schema:

Example Input:

Answer:

This query first truncates the 'submit_date' to the beginning of each month. Then it groups the data by the month and product ID. Next, it calculates the average star rating for each group. Finally, it orders the result by the month and average star rating in descending order. This provides a monthly overview of the average star ratings for each product.

Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur

SQL Question 5: Can you explain the distinction 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 an example of each one, say you had sales data exported from Equitable's Salesforce CRM stored in a datawarehouse which had two tables: and .

: retrieves rows from both tables where there is a match in the shared key or keys.

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

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

Here is an example of a SQL full outer join using the sales and tables:

SQL Question 6: Designing Database for Insurance Policies

Equitable is a well-known company specializing in insurance. You are asked to design a database that can store information about customers, their insurance policies, and tracks policy payments. These are the business requirements:

1. Customers have a unique customer ID, and we also track their name and email.
2. Customers can buy multiple insurance policies. Each policy has a unique policy ID, policy type (e.g., life, health, auto, home), and a policy start date.
3. For each policy, a log of all payments is necessary. The log includes payment date, amount paid, and the ID of the policy it belongs to.

Based on above requirements, construct database tables using markdown-formatted, including sample data.

Now, write a SQL query to find out all the customers who have made payments totaling more than $1000 on any of their insurance policies in the past year.

Example Data:

Example Data:

Example Data:

Answer:

This SQL query uses INNER JOIN to combine the three tables based on the relationships identified in the business requirements. It then filters out payments made over the last year using a WHERE clause. It groups the results by customer and uses a HAVING clause to filter out those groups where the total payment amount exceeds $1000, therefore retrieving all customers whose policy payments in the past year have totalled more than$1000.

SQL Question 7: What's a stored procedure, and why use one?

Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.

For a concrete example, suppose you were a Data Scientist on the Marketing Analytics team at Equitable. A task you would encounter freqently would be to calculate the conversion rate for Equitable's ads over a certain time period, for which you would write the following stored procedure:

To use this conversion rate stored procedure, you could call it like this:

SQL Question 8: Finding the Average Transaction Amount

Equitable is a bank, and you've been given access to their transactions table. Your task is to calculate the average transaction amount for each type of transaction (i.e., deposit, withdrawal, transfer) for the month of July 2022.

Example Input:

Answer:

This query first filters out transactions that did not occur during July 2022 with the WHERE clause. Then, it groups the data by the type of transaction and calculates the average transaction amount for each group by using the AVG() function.

To practice a very similar question try this interactive Stripe Repeated Payments Question which is similar for calculating aggregation on transaction data or this Uber User's Third Transaction Question which is similar for dealing with different types of transactions.

SQL Question 9: Find the Maximum Insured Value Per Category

As an insurance company, Equitable offers various types of insurance policies to its clients. Functioning within different categories such as 'home', 'auto', 'life', etc. Every policy has an insured value. For each category, can you write an SQL query to find the policy with the maximum insured value?

Consider the following sample data in the 'policies' table:

Example Input:

For this problem, you are expected to group the dataset by the column, and then find the maximum insured value present in each category.

Answer:

You can solve this problem using the and functions in SQL. Here is an example of how to do it in PostgreSQL:

This query groups the data by and then calculates the maximum within each of these groups. The function is used to accomplish this.

Example Output:

As seen from the result, it provides the maximum insured value for each category of policy.

SQL Question 10: What sets UNION apart from UNION ALL?

The operator merges the output of two or more statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.

For a concrete example, say you were a Data Analyst at Equitable working on a Marketing Analytics project. If you needed to get the combined result set of both Equitable's Google and Facebook ads you could execute this SQL query:

The operator works in a similar way to combine data from multiple statements, but it differs from the operator when it comes to handling duplicate rows. Whereas filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), outputs duplicate rows.

SQL Question 11: Filtering Customer Records

You have been asked to analyze the customer records database at Equitable. Your task is to find and list all customers whose names begin with "MC", which is a common pattern in some areas where the company operates.

The table is structured as follows:

Example Input:

You are expected to produce output in the following format, only including the columns of interest for this task, and ordered by last name:

Example Output:

Write a PostgreSQL query to solve this problem.

Answer:

This query filters the table to include only records where the field begins with 'Mc', which is achieved using the condition. The percent symbol ('%') in the condition acts as a wildcard, matching any characters that follow 'Mc'. The results are then ordered by in ascending order.

Preparing For The Equitable SQL Interview

The best way to prepare for a Equitable SQL interview is to practice, practice, practice. Besides solving the above Equitable SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like tech companies and insurance companies like Equitable.

Each SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there's an online SQL code editor so you can right in the browser run your SQL query answer and have it graded.

To prep for the Equitable SQL interview you can also be wise to solve SQL problems from other insurance companies like:

• Assurant SQL questions
• Mutual of Omaha SQL questions
• Alleghany SQL questions
• Unum Group SQL questions

Explore the latest news and announcements from Equitable, shaping the future of financial services!

However, if your SQL skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this free SQL tutorial.

This tutorial covers topics including manipulating string/text data and inner vs. outer JOIN – both of these pop up frequently in SQL interviews at Equitable.

Equitable Holdings Data Science Interview Tips

What Do Equitable Data Science Interviews Cover?

Besides SQL interview questions, the other question categories to practice for the Equitable Data Science Interview are:

• Probability & Stats Questions
• Python or R Programming Questions
• Open-Ended Data Case Studies
• ML Interview Questions
• Behavioral Based Interview Questions

How To Prepare for Equitable Data Science Interviews?

I think the best way to prepare for Equitable Data Science interviews is to read the book I wrote: Ace the Data Science Interview.

It solves 201 data interview questions taken from companies like Microsoft, Google & Amazon. The book's also got a refresher on SQL, Product-Sense & ML. And finally it's helped thousands of people land their dream job in data, which is why it's got over 1000+ 5-star reviews on Amazon.

While the book is more technical, it's also important to prepare for the Equitable behavioral interview. Start by understanding the company's values and mission.