11 Global Payments SQL Interview Questions (Updated 2024)
At Global Payments, SQL is used quite frequently for analyzing transactional database for behavior insights and managing their payment types database. That's why Global Payments almost always evaluates jobseekers on SQL problems during interviews for Data Science, Data Engineering and Data Analytics jobs.
To help you study for the Global Payments SQL interview, here’s 11 Global Payments SQL interview questions – how many can you solve?
11 Global Payments SQL Interview Questions
SQL Question 1: Analyzing Most Active Customers in Global Payments
Global Payments is a company that processes payments for businesses. One key type of user for Global Payments is an active user who makes frequent transactions. The goal would be to identify these power users who are critical for our business.
For the purposes of the SQL question, imagine we have a transactions table with the following schema and data:
Example Input:
| transaction_id | customer_id | transaction_date | amount |
|---|---|---|---|
| 5123 | 102 | 06/12/2022 | 200.00 |
| 7215 | 201 | 06/13/2022 | 300.00 |
| 3254 | 102 | 06/13/2022 | 500.00 |
| 9445 | 532 | 06/15/2022 | 150.00 |
| 6911 | 102 | 06/16/2022 | 250.00 |
Your task is to write a SQL query that identifies the top 5 customers with the most transactions in the last 30 days.
Answer:
This query will yield a list of customer IDs with their corresponding transaction counts during the last 30 days. The result will be ordered in descending order by the number of transactions, effectively listing the 'whale' users or power users of Global Payments. Customers who made more transactions will appear higher on the list.
Example Output:
| customer_id | transaction_count |
|---|---|
| 102 | 3 |
| 201 | 1 |
| 532 | 1 |
This output is based on the sample data and shows the customers and the number of transactions they conducted in the past 30 days in descending order.
To solve a similar VIP customer analysis question on DataLemur's free interactive SQL code editor, try this Microsoft SQL Interview problem:
SQL Question 2: Analyzing Transactional Data using SQL Window Functions
Global Payments, as a leading worldwide provider of payment technology services, deals with a vast amount of transactional data. Let's assume that we have a dataset named that contains multiple transactions per user, we want to calculate the running total amount of transactions each user performed each month.
The table contains the following columns:
- - A unique identifier for each transaction
- - Identifier for the user who performed the transaction
- - Date and time when the transaction was carried out
- - The dollar amount associated with each transaction
Example Input:
| transaction_id | user_id | transaction_date | amount |
|---|---|---|---|
| 1 | 1312 | 2023-01-07 00:00:00 | 150.00 |
| 2 | 1312 | 2023-02-11 00:00:00 | 425.00 |
| 3 | 1312 | 2023-03-05 00:00:00 | 205.00 |
| 4 | 1729 | 2023-01-08 00:00:00 | 100.00 |
| 5 | 1729 | 2023-02-14 00:00:00 | 400.00 |
We want the output to return the following:
- - The month portion of the
- - The running total of transaction amounts specified user made by the transaction_date of the month
Expected Output:
| user_id | transaction_month | running_total |
|---|---|---|
| 1312 | 1 | 150.00 |
| 1312 | 2 | 575.00 |
| 1312 | 3 | 780.00 |
| 1729 | 1 | 100.00 |
| 1729 | 2 | 500.00 |
Answer:
This query uses the window function which partitions the data by and in each partition, orders the rows by , and calculates the running total of . The function is used to obtain the month from the .
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 3: What does do?
The COALESCE() function can be used to replace NULL values with a specified value. For instance, if you are a Data Analyst at a company and are working on a customer analytics project to identify the most engaged customers, you may have access to the following data in the table:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | very_active |
| 601 | NULL | NULL |
Before you could procede with your analysis, you would need to remove the NULLs, and replace them with the default value for email engagement (not_active), and the default sms_engagement level (not_opted_in).
To do this, you'd run the following query:
This would get you the following output:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 102 | un-subscribed | not_opted_in |
| 103 | not_active | not_opted_in |
| 104 | not_active | very_active |
| 105 | very_active | very_active |
| 106 | not_active | not_opted_in |
SQL Question 4: Global Payments Transaction Analysis
Global Payments is a company assisting businesses with seamless, secure, and innovative payment solutions. One aspect they deal with is to process daily transactions for different merchants. For a given merchant, the company needs to find out the total amount transacted per day, based on the status of the transaction (successful/failed). Could you design a database that will facilitate this analysis?
Sample Input:
| merchant_id | merchant_name |
|---|---|
| 101 | MetroMart |
| 102 | HappyStore |
| 103 | ProlificMart |
Sample Input:
| transaction_id | merchant_id | transaction_date | transaction_status | transaction_amount |
|---|---|---|---|---|
| 2001 | 101 | 06/08/2022 00:00:00 | successful | $100.00 |
| 2002 | 101 | 06/08/2022 00:00:00 | failed | $50.00 |
| 2003 | 102 | 06/08/2022 00:00:00 | successful | $200.00 |
| 2004 | 101 | 06/09/2022 00:00:00 | successful | $300.00 |
| 2005 | 103 | 06/10/2022 00:00:00 | failed | $150.00 |
Answer:
Given this proposed data, a SQL query to solve the problem (in PostgreSQL dialect) would be:
This SQL query joins with on to get the merchant names. It then groups by , , and and calculates the sum of for each group.
SQL Question 5: What is database normalization?
Normalization involves breaking up your tables into smaller, more specialized ones and using primary and foreign keys to define relationships between them. Not only does this make your database more flexible and scalable, it also makes it easier to maintain. Plus, normalization helps to keep your data accurate by reducing the chance of inconsistencies and errors.
The only downside is now is that your queries will involve more joins, which are slow AF and often a DB performance botteleneck.
SQL Question 6: Filter Customer Payments
As a data analyst at Global Payments, you are tasked with finding all the customers who made a payment greater than $1000 and whose payment status is 'Completed'. Your manager wants this data sorted in descending order based on the payment amount. Also, the manager has specifically asked to only include the customers from the 'Business' category.
Please use the and tables given below:
Example Input:
| payment_id | customer_id | payment_date | amount | status |
|---|---|---|---|---|
| 6171 | 123 | 05/08/2022 00:00 | 1500 | Completed |
| 7802 | 265 | 06/10/2022 00:00 | 2500 | Pending |
| 5293 | 362 | 03/18/2022 00:00 | 5000 | Completed |
| 6352 | 192 | 04/26/2022 00:00 | 900 | Completed |
| 4517 | 981 | 07/05/2022 00:00 | 1200 | Completed |
Example Input:
| customer_id | first_name | last_name | category |
|---|---|---|---|
| 123 | Ben | Tenny | Business |
| 265 | Jerry | Springer | Individual |
| 362 | Tom | Jones | Business |
| 192 | Charlie | Cox | Individual |
| 981 | Jon | Snow | Business |
Answer:
This query first joins the table with table on . It then filters the data where the payment is greater than $1000, the is 'Completed', and the is 'Business'. The final step is to sort the data in descending order by the .
SQL Question 7: What's a stored procedure?
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 Global Payments. A task you would encounter freqently would be to calculate the conversion rate for Global Payments'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: Average Transaction Size
Global Payments is a company that provides merchant services and payment processing. The company works with businesses to facilitate transactions with consumers, including credit card processing and online payment solutions. For our scenario, you are given a database that contains information about transactions processed by Global Payments.
Here is your task: You are asked to find out the average transaction size in dollars for each client.
Example Input:
| transaction_id | client_id | transaction_date | transaction_amount |
|---|---|---|---|
| 101 | 001 | 06/08/2022 00:00:00 | 500 |
| 102 | 002 | 06/10/2022 00:00:00 | 700 |
| 103 | 001 | 06/18/2022 00:00:00 | 800 |
| 104 | 003 | 07/26/2022 00:00:00 | 600 |
| 105 | 001 | 07/05/2022 00:00:00 | 700 |
Example Input:
| client_id | client_name |
|---|---|
| 001 | Client A |
| 002 | Client B |
| 003 | Client C |
Example Output:
| client_name | avg_transaction_amount |
|---|---|
| Client A | 666.67 |
| Client B | 700.00 |
| Client C | 600.00 |
Answer:
In this SQL query, we join the and table on . Then, we group by and calculate the average of . This gives us the average transaction size for each client.
To practice a very similar question try this interactive Stripe Repeated Payments Question which is similar for handling transaction data or this Uber User's Third Transaction Question which is similar for analyzing individual transactions.
SQL Question 9: Analyzing Global Payments Transactions
As a data analyst for Global Payments, you are tasked with understanding the trends in transactional activities of different customers. Using the table that holds records of all transactions. Write a query to find the total amount processed for each customer every month. Also, provide the number of transactions for each total.
The table is structured as follows:
Example Input:
| transaction_id | customer_id | transaction_date | amount |
|---|---|---|---|
| 101 | 001 | 06/01/2022 | 100.00 |
| 102 | 002 | 06/10/2022 | 200.00 |
| 103 | 001 | 06/11/2022 | 150.00 |
| 104 | 003 | 07/02/2022 | 300.00 |
| 105 | 001 | 07/10/2022 | 200.00 |
Finally, generate a result that shows the , , and .
Expected Output:
| customer_id | month | total_amount | total_transactions |
|---|---|---|---|
| 001 | June | 250.00 | 2 |
| 002 | June | 200.00 | 1 |
| 003 | July | 300.00 | 1 |
| 001 | July | 200.00 | 1 |
Answer:
Below is the PostgreSQL query that would calculate the required data.
This query works by grouping rows based on and obtained from the . It then performs aggregate operations - for calculating total amount and for total transactions in each group.
SQL Question 10: What's an index, and what are the different types?
A database index is a data structure that improves the speed of data retrieval operations on a database table.
There are few different types of indexes that can be used in a database:
- Primary index: a unique identifier is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table.
- Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
- Clustered index: determines the physical order of the data in a table
For a concrete example, say you had a table of Global Payments customer payments with the following columns:
Here's what a clustered index on the column would look like:
A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values. This speeds up queries that filter or sort the data based on the , as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of June, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.
SQL Question 11: Analysing Global Payments Customer and Transactions Databases
Given two tables, and , write a SQL query to join these tables and find the total amount of payments made by each customer.
The table has the following schema:
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 1001 | James | Smith |
| 1002 | Maria | Garcia |
| 1003 | David | Johnson |
The table has the following schema:
Example Input:
| transaction_id | customer_id | transaction_date | amount |
|---|---|---|---|
| 125 | 1002 | 2022-07-25 00:00:00 | 250.00 |
| 126 | 1001 | 2022-07-25 00:00:00 | 150.00 |
| 127 | 1002 | 2022-07-26 00:00:00 | 200.00 |
| 128 | 1001 | 2022-07-29 00:00:00 | 300.00 |
| 129 | 1001 | 2022-07-30 00:00:00 | 400.00 |
Answer:
You can solve this problem by using a clause to combine the and tables, then using to group the results by customer, and finally using to calculate total amounts.
This SQL script will join the and tables on the column, group the results by customer, and sum the column to find the total transaction amount for each customer.
Example Output:
| first_name | last_name | total_amount |
|---|---|---|
| James | Smith | 850.00 |
| Maria | Garcia | 450.00 |
Since joins come up frequently during SQL interviews, try an interactive Spotify JOIN SQL question:
Preparing For The Global Payments 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.
In addition to solving the above Global Payments SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each problem on DataLemur has hints to guide you, step-by-step solutions and most importantly, there's an interactive coding environment so you can instantly run your query and have it executed.
To prep for the Global Payments SQL interview you can also be wise to practice interview questions from other tech companies like:
However, if your SQL coding skills are weak, forget about going right into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL topics like manipulating date/time data and LEAD/LAG window functions – both of which pop up routinely in Global Payments interviews.
Global Payments Data Science Interview Tips
What Do Global Payments Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions tested in the Global Payments Data Science Interview are:
- Probability & Statistics Questions
- Python Pandas or R Coding Questions
- Product Data Science Interview Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for Global Payments Data Science Interviews?
To prepare for Global Payments Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Google, Microsoft & tech startups
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