Data Scientists, Analysts, and Data Engineers at Western Union use SQL to analyze transaction data by identifying suspicious patterns and anomalies, for fraud detection, as well as structuring remittance data by tracking payment volumes, for business process optimization. That is the reason why Western Union asks SQL interview questions during job interviews.
So, to help you practice for the Western Union SQL interview, here’s 8 Western Union SQL interview questions in this article.
Your task is to write a SQL query to calculate the average amount of money transferred per month using Western Union services. You have access to a "transactions" table containing all transactions made by users.
transaction_id | user_id | transaction_date | amount |
---|---|---|---|
101 | 1081 | 2022-03-15 | 250 |
102 | 1732 | 2022-03-18 | 500 |
103 | 1081 | 2022-03-25 | 120 |
104 | 2656 | 2022-04-02 | 650 |
105 | 1732 | 2022-04-28 | 600 |
You need to return a table that shows the average transaction amount by month for all users.
month | avg_amount |
---|---|
3 | 290 |
4 | 625 |
This query first extracts the month from the transaction_date column using the EXTRACT function. It then groups by this month and calculates the average transaction amount for each group. The output is then ordered by month to show the result in a chronological order.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
Given a table of Western Union employee salary data, write a SQL query to find employees who make more than their own boss.
employee_id | name | salary | department_id | manager_id |
---|---|---|---|---|
1 | Emma Thompson | 3800 | 1 | |
2 | Daniel Rodriguez | 2230 | 1 | 10 |
3 | Olivia Smith | 8000 | 1 | 8 |
4 | Noah Johnson | 6800 | 2 | 8 |
5 | Sophia Martinez | 1750 | 1 | 10 |
8 | William Davis | 7000 | 2 | NULL |
10 | James Anderson | 4000 | 1 | NULL |
employee_id | employee_name |
---|---|
3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Code your solution to this problem directly within the browser on DataLemur:
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 solution above is hard to understand, you can find a detailed solution with hints here: Highly-Paid Employees.
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
Denormalization is done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.
For these OLAP use cases, you're bottleneck frequently is joining multiple tables, but de-normalizing your database cuts these outs.
Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with its own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.
As a Data Analyst for Western Union, you have been asked to analyze the average amount of money sent per transaction during a specific period. For the last month, please find the average amount sent for each sender in USD.
Please use the following as Example Input:
transaction_id | sender_id | receiver_id | transaction_date | amount_sent_usd |
---|---|---|---|---|
1001 | 101 | 201 | 04/01/2022 | 200.00 |
1002 | 102 | 202 | 04/02/2022 | 400.00 |
1003 | 101 | 203 | 04/03/2022 | 250.00 |
1004 | 103 | 204 | 04/04/2022 | 300.00 |
1005 | 101 | 201 | 04/05/2022 | 150.00 |
1006 | 102 | 205 | 04/06/2022 | 500.00 |
This SQL command calculates the average amount of money sent by each sender for transactions made in April 2022. The WHERE clause filters the transactions for the relevant period. The AVG function is used together with GROUP BY to calculate the average for each sender.
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 user-specific transaction analysis.
No, in 99% of normal cases a and do NOT produce the same result.
You can think of more as set addition when it combines the results of two tables.
You can think of a more as set multiplication, producing all combos of table 1 with combos of table 2.
You are hired as a data analyst at Western Union. The management wants to understand the customer transaction behavior and for this, they request you to calculate the monthly average transaction amount for each currency pair.
Assume the database has a table which has the following columns:
transaction_id | sender_currency | recipient_currency | transaction_amount | transaction_date |
---|---|---|---|---|
5021 | USD | EURO | 100 | 01/05/2022 |
6732 | USD | YEN | 200 | 01/05/2022 |
4373 | EURO | USD | 150 | 06/05/2022 |
8273 | YEN | EURO | 250 | 06/05/2022 |
3162 | YEN | USD | 300 | 07/05/2022 |
5463 | USD | EURO | 800 | 07/05/2022 |
This query works by first truncating the transaction date to the month and then calculating the average transaction amount for each currency pair (, ) for every month. The clause groups the data by the truncated and , . The clause then sorts the results by month and in descending order by the .
month | sender_currency | recipient_currency | average_amount |
---|---|---|---|
2022-05-01 | USD | EURO | 100 |
2022-05-01 | USD | YEN | 200 |
2022-05-01 | EURO | USD | 150 |
2022-05-01 | YEN | EURO | 250 |
2022-05-01 | YEN | USD | 300 |
2022-05-01 | USD | EURO | 800 |
Using a join in SQL, you can retrieve data from multiple tables and merge the results into a single table.
In SQL, there are four distinct types of JOINs. To demonstrate each kind, Imagine you had two database tables: an table that contains data on Google Ads keywords and their bid amounts, and a table with information on product sales and the Google Ads keywords that drove those sales.
: An INNER JOIN retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: A LEFT JOIN retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.
: A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.
: A FULL OUTER JOIN retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
Western Union wishes to perform some analytics on their customer transactions. The analytics department wants to find out all the transactions done by customers having the first name starting with 'Ro' in the last month (September 2022) in their 'transactions' database.
Sample Table:
transaction_id | customer_id | amount | transaction_date |
---|---|---|---|
65432 | 912 | 500 | 09/12/2022 |
67452 | 234 | 450 | 09/20/2022 |
87234 | 293 | 600 | 08/15/2022 |
74920 | 123 | 250 | 09/18/2022 |
96341 | 873 | 300 | 09/22/2022 |
Sample Table:
customer_id | first_name | last_name | |
---|---|---|---|
912 | Robert | Smith | robert292@gmail.com |
234 | Andy | Johnson | andyjohns@gmail.com |
293 | Rory | Williams | rory.will@gmail.com |
123 | Roger | Moore | roger_moore@gmail.com |
873 | Bill | Gates | billgates@outlook.com |
This SQL query selects all transactions from customers whose first name starts with 'Ro' that were processed in September 2022. The JOIN keyword is used to combine rows from two or more tables based on a related column between them (in this case, the customer_id). The LIKE 'Ro%' is used to filter the customer's first name and the date range is specified to focus on September 2022 only.
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Western Union SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the above Western Union SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each exercise has hints to guide you, step-by-step solutions and crucially, there's an interactive SQL code editor so you can right in the browser run your SQL query and have it executed.
To prep for the Western Union SQL interview it is also wise to practice interview questions from other financial services companies like:
Dive into the latest news and updates from Western Union and discover how they're shaping the future of global money transfers!
But if your SQL query skills are weak, forget about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL topics like window functions and WHERE with AND/OR/NOT – both of these come up frequently in SQL interviews at Western Union.
In addition to SQL query questions, the other topics tested in the Western Union Data Science Interview are:
To prepare for the Western Union Data Science interview have a deep understanding of the company's values and company principles – this will be important for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got: