8 Principal Financial SQL Interview Questions (Updated 2025)

At Principal Financial, SQL is crucial for analyzing financial data trends, including market performance and customer transactions, as well as managing client portfolio databases, such as account information, investment holdings, and risk profiles, which enables data-driven decision making and personalized financial planning. Because of this, Principal Financial often asks SQL questions during interviews for Data Analyst, Data Science, and BI jobs.

So, to help you study for the Principal Financial SQL interview, we've collected 8 Principal Financial SQL interview questions – can you solve them?

8 Principal Financial SQL Interview Questions

SQL Question 1: Identify Power Users at Principal Financial

Principal Financial is a global investment management company, and their customers are people who invest with them. A customer's importance comes from the frequency and amount of their investments. We want to identify power users who are most important to the business.

A power user at Principal Financial is defined as a customer who has made investments more than once in the calendar month, with an overall investment amount for the month greater than $10,000.

Given the investments table below, please write a SQL query to identify such power users. Each row in the table signifies one investment transaction.

investments Example Input

Answer

SELECT 
  DATE_PART('month', transaction_date) AS month, 
  DATE_PART('year', transaction_date) AS year,
  customer_id, 
  COUNT(*) AS transaction_count, 
  SUM(investment_amount) AS total_investment 
FROM 
  investments 
GROUP BY 
  year, month, customer_id 
HAVING 
  COUNT(*) > 1 AND SUM(investment_amount) > 10000 
ORDER BY 
  year, month, total_investment DESC, transaction_count DESC;

This SQL query groups the investments table by month, year, and customer_id. It then counts the number of transactions for each group and also sums up the investment amounts. It then filters out those groups where the transaction count is greater than 1 and the total investment amount is greater than $10,000. Finally, it orders the resulting groups by year, month and by total investment and transaction count in the descending order.

To practice a super-customer analysis question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:

SQL Question 2: Top 3 Department Salaries

Suppose there was a table of Principal Financial employee salary data. Write a SQL query to find the top 3 highest earning employees in each department.

Principal Financial employees Example Input:

department Example Input:

Example Output:

Try this 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.

WITH ranked_salary AS (
  SELECT
    name,
    salary,
    department_id,
    DENSE_RANK() OVER (
      PARTITION BY department_id ORDER BY salary DESC) AS ranking
  FROM employee
)


SELECT
  d.department_name,
  rs.name,
  rs.salary
FROM ranked_salary AS rs
INNER JOIN department AS d
   ON rs.department_id = d.department_id
WHERE rs.ranking <= 3
ORDER BY d.department_id, rs.salary DESC, rs.name ASC;

If the code above is hard to understand, you can find a detailed solution with hints here: Top 3 Department Salaries.

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 Principal 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 EXPLAIN command and understand a bit more about a query-execution plan.

SQL Question 4: Calculate Rolling Average of Sales Revenue

Description

Principal Financial provides a variety of financial products and services to individuals and businesses. Including insurance plans, retirement solutions, and investment options.

Assume that you are interviewing for their finance and investment department. You are given a table of their product sales data, and you need to write a SQL query to calculate the 3-month rolling average sales revenue for each product.

sales_data Example Input:

Example Output:

Answer:

SELECT year_month, product_id,
   AVG(sales_revenue) OVER (
		PARTITION BY product_id ORDER BY year_month ROWS 2 PRECEDING
	) as rolling_avg_revenue
FROM sales_data
ORDER BY product_id, year_month;

In this query, Windows Function AVG() OVER (PARTITION BY product_id ORDER BY year_month ROWS 2 PRECEDING) is used to calculate the 3 months rolling average. This partition is done by product_id, and ordering is based on year_month. It then considers the current row and the 2 preceding rows for calculating the average, providing the 3 months rolling average sales revenue.

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

SQL Question 5: What is a cross-join, and when would you use one?

A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.

Suppose you were building a Neural Network ML model, that tried to score the probability of a customer buying a Principal Financial product. Before you started working in Python and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Principal Financial products.

Here's a cross-join query you could use to find all the combos:

SELECT customers.id AS customer_id, principal_financial_products.id AS product_id
FROM customers
CROSS JOIN principal_financial_products;

Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. For example, if you had 10,000 potential customers, and Principal Financial had 500 different product SKUs, you'd get 5 million rows as a result!!

SQL Question 6: Click-Through and Conversion Rates for Principal Financial Group

Consider we are working for Principal Financial Group, a global financial investment management leader. To optimize our online services, we often monitor how visitors interact with our web pages. For instance, we look for the click-through rate of our digital banners, and the conversion rate from viewing our financial products to adding a product to the virtual wishlist. Calculate the click-through and conversion rates for our digital banners and financial products.

Given the following tables, write a SQL query that calculates:

1. The click-through rate (CTR) of the digital banners, defined as the number of clicks on the banner divided by the number of views, for each banner.
2. The conversion rate of the financial products, defined as the number of times a product is added to the wishlist divided by the number of times it is viewed, for each product.

banners Example Input:

banner_activity Example Input:

products Example Input:

product_activity Example Input:

Answer:

WITH banner_cte AS (
SELECT 
    banner_id, 
    COUNT(CASE WHEN action='click' THEN 1 END) AS clicks, 
    COUNT(CASE WHEN action='view' THEN 1 END) AS views 
FROM banner_activity 
GROUP BY banner_id
),

product_cte AS (
SELECT 
    product_id,
    COUNT(CASE WHEN action='add_to_wishlist' THEN 1 END) AS adds, 
    COUNT(CASE WHEN action='view' THEN 1 END) AS views
FROM product_activity
GROUP BY product_id
)

SELECT 
    b.banner_id,
    banners.banner_name,
    (b.clicks::float/b.views) AS CTR,
    p.product_id,
    products.product_name,
    (p.adds::float/p.views) AS Conversion_Rate
FROM 
    banner_cte b
JOIN
    product_cte p
ON
    b.banner_id = p.product_id
JOIN
    banners
ON
    banners.banner_id = b.banner_id
JOIN
    products
ON
    products.product_id = p.product_id;

This query first calculate the click and view counts for each banner and product in the banner_cte and product_cte common table expressions (CTEs). Then, it joins these CTEs with the banners and products tables to get banner and product names and finally calculates the CTR and Conversion Rate. Note that the count is explicitly cast to float to ensure accurate division results.

To practice another question about calculating rates, solve this TikTok SQL question on DataLemur's online SQL coding environment:

SQL Question 7: What's the purpose of the FOREIGN KEY constraint?

A FOREIGN KEY is a field in a table that references the PRIMARY KEY of another table. It creates a link between the two tables and ensures that the data in the FOREIGN KEY field is valid.

Say for example you had sales analytics data from Principal Financial's CRM (customer-relationship management) tool.

CREATE TABLE principal_financial_accounts (
    account_id INTEGER PRIMARY KEY,
    account_name VARCHAR(255) NOT NULL,
    industry VARCHAR(255) NOT NULL
);

CREATE TABLE opportunities (
    opportunity_id INTEGER PRIMARY KEY,
    opportunity_name VARCHAR(255) NOT NULL,
    account_id INTEGER NOT NULL,
    FOREIGN KEY (account_id) REFERENCES principal_financial_accounts(account_id)
);

The FOREIGN KEY constraint ensures that the data in the account_id field of the "opportunities" table is valid, and prevents the insertion of rows in the opportunities table that do not have corresponding entries in the principal_financial_accounts table. It also helps to enforce the relationship between the two tables and can be used to ensure that data is not deleted from the accounts table if there are still references to it in the opportunities table.

SQL Question 8: Employees' Adjusted Salary Calculation

Principal Financial maintains a payroll database where each employee's basic salary and bonuses are recorded. The company wants to calculate the adjusted salaries of the employees. Adjusted salary takes into account the taxable portion which is 75% of the basic salary, adds any bonuses and then rounds the amount to the nearest whole number. In addition, it calculates the square root of the annual salary to see their benefits eligibility which applies if the square root is less than 280.

Given two tables employee and salary, where employee has emp_id, first_name, and last_name as columns and salary has emp_id, basic_salary, bonus as columns. Write a PostgreSQL query which outputs emp_id, first_name, last_name, adjusted_salary, and benefits_eligibility columns.

employee Table:

salary Table:

Answer:

SELECT 
    e.emp_id,
    e.first_name,
    e.last_name,
    ROUND((s.basic_salary * 0.75) + s.bonus) as adjusted_salary,
    CASE 
        WHEN SQRT(ROUND((s.basic_salary * 0.75) + s.bonus) * 12) < 280 THEN 'Eligible'
        ELSE 'Not Eligible'
    END as benefits_eligibility
FROM 
    employee e
JOIN 
    salary s 
ON 
    e.emp_id = s.emp_id;

The query first calculates the taxable portion of the salary and adds any existing bonuses to it then rounds off the final figure providing us with 'adjusted_salary'. We then calculate the square root of the annual adjusted salary to determine 'benefits_eligibility'. The POSTGRESQL function SQRT is used to determine the square root and ROUND function is used to round off decimals. The result of this calculation is then compared with the value 280 and a CASE statement is used to determine if employees are 'Eligible' or 'Not Eligible' for benefits.

To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for involving calculations and rounding results to a certain decimal place or this JPMorgan Chase Cards Issued Difference Question which is similar for < use of aggregate functions to assess financial-related information.

How To Prepare for the Principal Financial SQL Interview

The best way to prepare for a Principal Financial SQL interview is to practice, practice, practice. Beyond just solving the above Principal Financial SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Microsoft, Google, Amazon, and tech startups.

Each problem on DataLemur has hints to guide you, full answers and best of all, there's an interactive SQL code editor so you can instantly run your SQL query answer and have it executed.

To prep for the Principal Financial SQL interview you can also be useful to practice SQL questions from other insurance companies like:

• AmFam SQL questions
• Guardian Life SQL questions
• Loews SQL questions
• Reinsurance Group of America SQL questions

Explore the latest news releases from Principal Financial and discover what's driving their growth!

But if your SQL foundations are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers SQL concepts such as rank window functions and filtering strings based on patterns – both of these pop up often in SQL interviews at Principal Financial.

Principal Financial Data Science Interview Tips

What Do Principal Financial Data Science Interviews Cover?

In addition to SQL interview questions, the other topics covered in the Principal Financial Data Science Interview include:

• Stats Interview Questions
• Python or R Programming Questions
• Analytics and Product-Metrics Questions
• ML Modelling Questions
• Behavioral & Resume-Based Questions

How To Prepare for Principal Financial Data Science Interviews?

To prepare for the Principal Financial Data Science interview make sure you have a deep understanding of the company's values and mission – this will be clutch for acing the behavioral interview. For technical interviews get ready by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from Google, Microsoft & tech startups
• A Crash Course covering Python, SQL & ML
• Great Reviews (1000+ 5-star reviews on Amazon)