9 USAA SQL Interview Questions (Updated 2025)

USAA employees write SQL queries for extracting and analyzing customer data, including financial transaction history and credit scores, to optimize financial products. It is also used for handling large datasets for fraud detection and prevention by identifying suspicious transactions, which is why USAA includes SQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.

So, to help you prep, we've collected 9 USAA SQL interview questions – can you solve them?

9 USAA SQL Interview Questions

SQL Question 1: Analyzing Customer Product Reviews

USAA is interested in understanding the trends of products based on the stars given by customers in their reviews. To achieve this, they would like to analyze the average stars given to each product monthly. Write a SQL query to create a table that shows the month, product, and average stars for each product per month.

Please note that submit_date format is 'MM/DD/YYYY HH24:MI:SS' and you are required to use a SQL window function in your solution.

reviews Example Input:

Example Output:

Answer:

SELECT
    EXTRACT(MONTH FROM submit_date::TIMESTAMP) AS month,
    product_id AS product,
    AVG(stars) OVER(
        PARTITION BY 
            product_id,
            EXTRACT(MONTH FROM submit_date::TIMESTAMP)
    ) as avg_stars
FROM reviews
GROUP BY month, product;

This solution creates a window that groups data by product_id and the month extracted from submit_date. It then calculates the average stars for each group (i.e., for each product per month). The AVG function is applied to compute the average within each window partition. This provides USAA with a monthly understanding of product performance as judged by their customers.

p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur

SQL Question 2: Top Department Salaries

Given a table of USAA employee salaries, write a SQL query to find the top 3 highest paid employees in each department.

USAA employees Example Input:

department Example Input:

Example Output:

Try 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.

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 confusing, you can find a detailed solution with hints here: Top 3 Department Salaries.

SQL Question 3: Can you explain the distinction between a correlated and a non-correlated sub-query?

While a correlated subquery relies on columns in the main query's FROM clause and cannot function independently, a non-correlated subquery operates as a standalone query and its results are integrated into the main query.

An example correlated sub-query:

SELECT name, salary
FROM usaa_employees e1
WHERE salary > (SELECT AVG(salary) FROM  usaa_e2 WHERE e1.department = e2.department);

This correlated subquery retrieves the names and salaries of USAA employees who make more than the average salary for their department. The subquery references the department column in the main query's FROM clause (e1.department) and uses it to filter the rows of the subquery's FROM clause (e2.department).

An example non-correlated sub-query:

SELECT name, salary
FROM usaa_employees
WHERE salary > (SELECT AVG(salary) FROM usaa_employees WHERE department = 'Data Science');

This non-correlated subquery retrieves the names and salaries of USAA employees who make more than the average salary for the Data Science department (which honestly should be very few people since Data Scientists are awesome and deserve to be paid well).The subquery is considered independent of the main query can stand alone. Its output (the average salary for the Data Science department) is then used in the main query to filter the rows of the USAA employees table.

SQL Question 4: USAA Insurance Claims Management

USAA is widely known for its auto insurance services. As part of their services, they deal with numerous insurance claims. Design a database system to handle the management of auto insurance claims. The database needs to track details about policies, customers, and vehicles involved, as well as specifics of each claim - like date of incident, description, status of claim, and the amount. Based on this initial design, how would you retrieve all claims of a specific customer by their customer_id?

Please consider the following tables for the task:

customers Example Input:

policies Example Input:

vehicles Example Input:

claims Example Input:

Answer:

SELECT c.*, p.*, v.*, cl.* 
FROM customers c
JOIN policies p ON c.customer_id = p.customer_id
JOIN vehicles v ON p.policy_id = v.policy_id
JOIN claims cl ON p.policy_id = cl.policy_id
WHERE c.customer_id = <ENTER_CUSTOMER_ID_HERE>;

This query would return all insurance claims for a specific customer. It joins the four tables on the appropriate foreign key and filters the results based on a customer_id. Replace <ENTER_CUSTOMER_ID_HERE> with the specific customer_id you want to filter by.

SQL Question 5: Consider unique indexes, and non-unique indexes. What are some similarities and differences?

Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.

Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.

Unique Indexes are blazing fast. Non unique indexes can improve query performance, but they are often slower because of their non-unique nature.

SQL Question 6: Filtering USAA Customers Based on their Status and Recent Transactions

As a part of USAA's data analysis team, your task is to retrieve the record details of all the customers who are 'Active' status and have completed a transaction greater than $5000 within the last 30 days. Also, you need to provide the count of such customers.

customer Example Input:

transaction_details Example Input:

Example Output:

Count of customers meeting conditions: 1

Answer:

SELECT c.customer_id, c.first_name, c.last_name, t.amount
FROM customer c
JOIN transaction_details t
ON c.customer_id = t.customer_id
WHERE c.status = 'Active' AND t.transaction_date > CURRENT_DATE - INTERVAL '30 days' AND t.amount > 5000;

SELECT COUNT(*)
FROM customer c
JOIN transaction_details t
ON c.customer_id = t.customer_id
WHERE c.status = 'Active' AND t.transaction_date > CURRENT_DATE - INTERVAL '30 days' AND t.amount > 5000;

In the first query, we are joining the 'customer' and 'transaction_details' tables using the 'customer_id' field which is present in both the tables. We then filter the records based on the conditions specified - customers should be 'Active', transaction date should be within the last 30 days and the transaction amount should be greater than $5000.

The second query returns the count of such customers. It has the same conditions as the first query but instead of returning the customer details, it returns the count of such records.

SQL Question 7: What's the UNION operator do, and can you give an example?

UNION is used to combine the output of multiple SELECT statements into one big result!

For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at USAA, 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 UNION in the following way:

SELECT email, job_title, company_id
FROM usaa_sfdc_leads
WHERE created_at > '2023-01-01';

UNION

SELECT email, job_title, company_id
FROM usaa_hubspot_leads
WHERE created_at > '2023-01-01'

SQL Question 8: Analyzing Click-through-Rates for USAA

USAA, a company providing a range of financial products and services, uses digital advertising to attract potential customers. They track user interactions from viewing an ad to eventually signing up for a service.

You are given two tables: clicks and signups. The clicks table records every time a user clicks on one of USAA's ads. The signups table records every time a user signs up for USAA's services after clicking an ad.

Determine the click-through conversion rate, which is the number of sign-ups divided by the number of clicks.

clicks Example Input:

signups Example Input:

Answer:

SELECT 
    COUNT(DISTINCT signups.user_id)::float / COUNT(DISTINCT clicks.user_id)::float AS click_through_conversion_rate
FROM 
    clicks
LEFT JOIN 
    signups ON clicks.user_id = signups.user_id;

This SQL query computes the click-through conversion rate by dividing the distinct count of user IDs in the signups table by the distinct count of user IDs in the clicks table. The LEFT JOIN ensures that all clicks are included in the calculation, regardless whether or not they resulted in a signup. This provides the proportion of users who clicked on an ad and subsequently signed up for a service, giving an indication of the effectiveness of the advertising campaign.

To solve a similar problem about calculating rates, solve this TikTok SQL question on DataLemur's online SQL coding environment:

SQL Question 9: Average Insurance Claim

USAA provides insurance for many vehicles. They typically track the insurance claims made by users for each vehicle type. For this question, assume the following scenario: you have a table claims which has columns for claim_id, user_id, claim_date, vehicle_type (categories like 'Car', 'Boat', 'Motorcycle', etc.), and claim_amount.

Write a SQL query that finds the average claim amount for each vehicle type for the year 2021. List the vehicle types in descending order of the average claim amount.

claims Example Input:

Answer:

SELECT 
    vehicle_type,
    AVG(claim_amount) AS avg_claim_amount
FROM 
    claims
WHERE 
    DATE_PART('year', claim_date) = 2021
GROUP BY 
    vehicle_type
ORDER BY 
    avg_claim_amount DESC;

This PostgreSQL query first filters the claims to include only those made in the year 2021. It then groups the claims by vehicle type and calculates the average claim amount for each group. The results are then ordered in descending order of the average claim amount, so the vehicle type with the highest average claim amount will be listed first.

Example Output:

Preparing For The USAA SQL Interview

The key to acing a USAA SQL interview is to practice, practice, and then practice some more! In addition to solving the above USAA SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.

Each exercise has multiple hints, step-by-step solutions and best of all, there is an interactive coding environment so you can right online code up your query and have it checked.

To prep for the USAA SQL interview it is also a great idea to solve SQL problems from other insurance companies like:

• TIAA SQL questions
• Progressive SQL questions
• Liberty Mutual SQL questions
• New York Life SQL questions

See how USAA's automated investment platform can help you make informed investment decisions with confidence!

But if your SQL skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this free SQL tutorial.

This tutorial covers SQL topics like SQL joins with practice exercises and CTEs – both of these show up frequently during USAA interviews.

USAA Data Science Interview Tips

What Do USAA Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems covered in the USAA Data Science Interview are:

• Experimental Design & AB Testing Questions
• Python Pandas or R Coding Questions
• Analytics and Product-Metrics Questions
• ML Modelling Questions
• Behavioral Based Interview Questions

How To Prepare for USAA Data Science Interviews?

To prepare for the USAA Data Science interview make sure you have a strong understanding of the company's values and company principles – 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 FAANG tech companies
• A Refresher on SQL, Product-Sense & ML
• Great Reviews (1000+ reviews, 4.5-star rating)