10 Visa SQL Interview Questions (Updated 2025)

Data Scientists, Analysts, and Data Engineers at Visa uses SQL for extracting insights from transactional databases to detect fraudulent activity, such as identifying suspicious patterns and anomalies. It is also used for compiling customer spending data to provide insights into user behavior, including purchase habits and preferences. Because of this, Visa asks prospective hires SQL interview problems.

Thus, to help you prep, we've curated 10 Visa SQL interview questions – can you solve them?

10 Visa SQL Interview Questions

SQL Interview Question 1: ApplePay Volume

Visa is analysing its partnership with ApplyPay. Calculate the total transaction volume for each merchant where the transaction was performed via ApplePay.

Output the merchant ID and the total transactions. For merchants with no ApplePay transactions, output their total transaction volume as 0. Display the result in descending order of the transaction volume.

Tip: You might want to check the spelling of the payment methods... just sayin' 👀

transactions Table:

transactions Example Input:

Example Output:

Answer:

SELECT 
  merchant_id, 
  SUM(CASE WHEN LOWER(payment_method) = 'apple pay' THEN transaction_amount
  ELSE 0 END) AS total_transaction
FROM transactions
GROUP BY merchant_id
ORDER BY total_transaction DESC;

To practice this Visa SQL Interview question and engage with other Data Scientists go to DataLemur!

SQL Question 2: Monthly Merchant Balance

Say you have access to all the transactions for a given merchant account. Write a query to print the cumulative balance of the merchant account at the end of each day, with the total balance reset back to zero at the end of the month. Output the transaction date and cumulative balance.

transactions Table:

transactions Example Input:

Example Output:

Answer:

WITH daily_balances AS (
  SELECT
    DATE_TRUNC('day', transaction_date) AS transaction_day,
    DATE_TRUNC('month', transaction_date) AS transaction_month,
    SUM(CASE WHEN type = 'deposit' THEN amount
      WHEN type = 'withdrawal' THEN -amount END) AS balance
  FROM transactions
  GROUP BY 
    DATE_TRUNC('day', transaction_date),
    DATE_TRUNC('month', transaction_date))

SELECT
  transaction_day,
  SUM(balance) OVER (
    PARTITION BY transaction_month
    ORDER BY transaction_day) AS balance
FROM daily_balances
ORDER BY transaction_day;

Code your solution to this Visa SQL problem interactively on DataLemur:

SQL Question 3: What are the similarities and difference between relational and non-relational databases?

While both types of databases are used to store data (no duh!), relational databases and non-relational (also known as NoSQL databases) differ in a few important ways, most importantly on the way data is stored. Relational databases use a data model consisting of tables and rows, while NoSQL databases use a variety of data models, including document, key-value, columnar, and graph storage formats.

This added flexibilty makes NoSQL databases great for non-tabular data (like hierarchal data or JSON data), or data where the type/format is constantly evolving. With this added flexibility, comes one big weakness – you won't get ACID-compliance. That means, unlike relational databases which are typically adhere to the ACID properties (atomic, consistent, isolated, and durable), you don't get as strong guarentees with most non-relational databases.

SQL Question 4: Calculate the Total Number of Transactions and Average Transaction Amount per Country

Visa Inc. is an American multinational financial services corporation that facilitates electronic funds transfers throughout the world, most commonly through Visa-branded credit cards, debit cards and prepaid cards.

In this SQL interview question, your task is to write a SQL query that calculates the total number of transactions and average transaction amount each month for each country, and rank them within each country based on the total transaction count.

Suppose we have the following transaction data.

transactions Example Input:

We would like to output data in the following format.

Example Output:

Answer:

WITH monthly_stats AS (
    SELECT
        EXTRACT(MONTH FROM transaction_date) AS mth,
        country,
        COUNT(transaction_id) AS total_transactions,
        AVG(amount) AS average_amount
    FROM transactions
    GROUP BY mth, country
)
SELECT
     mth,
     country,
     total_transactions,
     average_amount,
     RANK() OVER(PARTITION BY country ORDER BY total_transactions DESC) AS rank
FROM monthly_stats
ORDER BY mth, country;

This query first calculates the total transaction count and average transaction amount per month for each country, and then ranks the months within each country based on the total transaction count. By using the window function RANK() OVER(PARTITION BY country ORDER BY total_transactions DESC), we can get a ranking of the months within each country based on the total transaction count. The ORDER BY mth, country at the end sorts the output by the month and country.

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

SQL Question 5: Could you provide a list of the join types in SQL and explain what each one does?

In SQL, a join retrieves rows from multiple tables and combines them into a single result set.

Four JOIN types can be found in SQL. For an example of each one, suppose you had a table of Visa orders and Visa customers.

1. INNER JOIN: Rows from both tables are retrieved when there is a match in the shared key or keys. An INNER JOIN between the Orders and Customers tables would return only rows where the customer_id in the Orders table matches the customer_id in the Customers table.

2. LEFT JOIN: A LEFT JOIN retrieves all rows from the left table (in this case, the Orders table) and any matching rows from the right table (the Customers table). If there is no match in the right table, NULL values will be returned for the right table's columns.

3. RIGHT JOIN: A RIGHT JOIN combines all rows from the right table (in this case, the Customers table) and any matching rows from the left table (the Orders table). If there is no match in the left table, NULL values will be displayed for the left table's columns.

4. FULL OUTER JOIN: A FULL OUTER JOIN combines 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 displayed for the columns of the non-matching table.

SQL Question 6: Design Visa's Transaction Database

Suppose you are given the task to design a database tracking all Visa's card transactions worldwide. The database should track the following information:

• Cardholders (with their unique ID, name, and country),
• Transaction details (unique transaction ID, amount in local currency, date/time, the card used, and the merchant's ID),
• Merchants (with their unique ID, name, city, and country).

cardholder Sample Input:

transaction Sample Input:

merchant Sample Input:

Please write a PostgreSQL query to: 1). retrieve all cardholders who have made transactions with the total amount over $1000 in their own country's stores in July 2022, 2). get the total transaction amount and the count of transactions for each of these cardholders.

Answer:

SELECT 
  c.cardholder_id,
  c.name, 
  SUM(t.amount) AS total_transaction_amount, 
  COUNT(t.transaction_id) AS transaction_count
FROM cardholder c 
JOIN transaction t ON c.cardholder_id = t.card_id 
JOIN merchant m ON t.merchant_id = m.merchant_id 
WHERE c.country = m.country 
  AND DATE(t.transaction_date) BETWEEN '2022-07-01' AND '2022-07-31'
GROUP BY c.cardholder_id, c.name
HAVING SUM(t.amount) > 1000
ORDER BY total_transaction_amount DESC;

This query joins the cardholder, transaction, and merchant tables based on the relationships defined amongst them. It filters transactions within July 2022 and made within cardholders' home countries. Aggregation methods SUM and COUNT calculate the total transaction amount and the transaction count per cardholder, respectively. The HAVING clause filters out cardholders whose total transaction amount does not exceed $1000.

SQL Question 7: Can you explain what MINUS / EXCEPT SQL commands do?

The MINUS/EXCEPT operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.

Note that EXCEPT is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't worry about knowing which DBMS supports which exact commands since Visa interviewers aren't trying to trip you up on memorizing SQL syntax).

For a tangible example of EXCEPT in PostgreSQL, suppose you were doing an HR Analytics project for Visa, and had access to Visa's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables.

You could use EXCEPT operator to find all contractors who never were a employee using this query:

SELECT first_name, last_name
FROM visa_contractors

EXCEPT

SELECT first_name, last_name
FROM visa_employees

SQL Question 8: Average Daily Transaction Volume for Each Merchant.

As a data analyst at Visa, your task will be to analyze the average daily transaction volume for each merchant for the past month. Given a table 'Transactions', determine the average number of daily transactions per merchant for the specified time period.

Transactions Example Input:

Answer:

SELECT 
    merchant_id,
    date_trunc('day', transaction_date) as day,
    COUNT(*) as transactions_count
FROM 
    Transactions
WHERE
    transaction_date >= (now() - interval '1 month')
GROUP BY 
    1, 2
ORDER BY 
    avg_transactions DESC;

In this query, we first filter the Transactions table to include only transactions from the past month. Then, we group by merchant_id and day, and count the number of transactions for each grouping. The average number of daily transactions per merchant is then computed by ordering in descending order based on the number of transactions for each day. The date_trunc function is used to strip off any time components and return just the date allowing grouping by the day.

To practice a very similar question try this interactive Stripe Repeated Payments Question which is similar for analyzing transaction data or this Uber User's Third Transaction Question which is similar for < calculating transaction count.

SQL Question 9: Understanding Click-through Rates for Visa Marketing Campaigns

To optimise online conversions, Visa is interested in understanding click-through rates (CTR) of their different marketing campaigns. Assume that a 'click' is when a user clicks on an advertisement to visit Visa's website, and a 'conversion' is when the user takes a specific action on the website, e.g., applying for a credit card.

Here is a set of sample data for this problem. We have two tables, clicks and conversions.

clicks Example Input:

conversions Example Input:

For each campaign, calculate the number of clicks, the number of conversions and the click-through rate (CTR), which is defined as the number of conversions divided by the number of clicks.

Answer:

Here is a SQL query that does this calculation:

SELECT 
    c.campaign_id, 
    COUNT(DISTINCT c.click_id) AS num_clicks, 
    COUNT(DISTINCT v.conversion_id) AS num_conversions, 
    (COUNT(DISTINCT v.conversion_id)::float / COUNT(DISTINCT c.click_id)::float) AS CTR
FROM 
    clicks c 
LEFT JOIN 
    conversions v 
ON 
    c.user_id = v.user_id 
AND 
    c.campaign_id = v.campaign_id 
GROUP BY 
    c.campaign_id;

This query first joins the clicks and conversions tables on both user_id and campaign_id, then it calculates the click-through rate by dividing the number of unique conversions by the number of unique clicks for each campaign. The ::float cast is used to ensure that the division operation returns a floating point number.

To solve another question about calculating rates, solve this SQL interview question from TikTok on DataLemur's online SQL code editor:

SQL Question 10: What are the similarities and differences between a clustered index and non-clustered index?

Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.

The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.

Preparing For The Visa SQL Interview

The key to acing a Visa SQL interview is to practice, practice, and then practice some more! Beyond just solving the above Visa SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Uber, and Microsoft.

Each interview question has multiple hints, step-by-step solutions and best of all, there is an interactive SQL code editor so you can right online code up your SQL query answer and have it checked.

To prep for the Visa SQL interview it is also a great idea to solve SQL problems from other payment & fintech companies like:

• Mastercard SQL questions
• Western Union SQL questions
• Stripe SQL questions
• PayPal SQL questions

Stay ahead of the curve with Visa's latest news and announcements, shaping the future of digital payments!

However, if your SQL coding skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.

This tutorial covers things like window functions like RANK() and ROW_NUMBER() and SQL joins with practice exercises – both of which pop up often in Visa SQL assessments.

Visa Data Science Interview Tips

What Do Visa Data Science Interviews Cover?

In addition to SQL interview questions, the other types of problems covered in the Visa Data Science Interview include:

• Statistics and Probability Questions
• Python Pandas or R Coding Questions
• Analytics and Product-Metrics Questions
• Machine Learning Questions
• Resume-Based Behavioral Questions

How To Prepare for Visa Data Science Interviews?

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

It solves 201 interview questions sourced from tech companies like Netflix, Google, & Airbnb. The book's also got a crash course covering Product Analytics, SQL & 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 key to prepare for the Visa behavioral interview. A good place to start is by reading the company's values and company principles.