# 10 Payoneer SQL Interview Questions (Updated 2024)

Updated on

March 3, 2024

At Payoneer, SQL is used day-to-day for analyzing transactional data to forecast trends, and to generate insights into fraudulent payments. Unsurprisingly this is why Payoneer almost always evaluates jobseekers on SQL problems during interviews for Data Science, Analytics, and & Data Engineering jobs.

To help you practice for the Payoneer SQL interview, this blog covers 10 Payoneer SQL interview questions – able to answer them all?

## 10 Payoneer SQL Interview Questions

### SQL Question 1: Identify Top Transaction Users in Payoneer

For an online payment company like Payoneer, one of the key measures of a "power" or VIP user could be the total transaction amount over a specific period. This SQL question asks you to write a query to identify the top 5 users who conducted transactions with the highest total amount over the past month.

Provide this type of structured data by considering two tables; which keeps records of each transaction conducted by a user, and which holds basic user information.

##### Example Input:
transaction_iduser_idtransaction_dateamount
110108/01/2022 00:00:00500
210208/15/2022 00:00:00200
310308/10/2022 00:00:001000
410108/20/2022 00:00:00500
510408/25/2022 00:00:00400
##### Example Input:

This PostgreSQL query joins the and tables based on the and filters out the transactions that took place in the current month. Then it groups the resulting data by , calculates the sum of for each user and orders them in descending order of the total transaction amount. The statement at the end ensures that only the top 5 users are selected.

To practice a similar power-user data analysis problem question on DataLemur's free interactive SQL code editor, try this recently asked Microsoft SQL interview question:

### SQL Question 2: Calculate the Average Load Time for Each Day

As an analytical employee of Payoneer, you have been given the task of inspecting the performance of the Payoneer customer's webpage loading times. Given a data set of dates, customers, and their respective webpage load times, write a SQL query to calculate the average load time per day for each customer.

To make it more interesting, we would also like to know the customer's previous day's average load time, for comparison purposes.

Take the following tables as an example:

##### Example Input:
2022-08-0110013.5
2022-08-0110012.3
2022-08-0210024.7
2022-08-0210013.2
2022-08-0210012.4
##### Example Output:
2022-08-0110012.90
2022-08-0210012.82.9
2022-08-0210024.70

The above PostgreSQL query calculates the average load time for each customer for each day. It then uses the window function to find the previous day's average load time, if there is one. If there is no previous day (i.e., it's the customer's first day), the query returns 0, as there is no data available for comparison.

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

### SQL Question 3: What is a cross-join?

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.

Let's say you were building a Machine Learning model that attempts to score the probability of a customer purchasing a Payoneer product. Before working in Pandas and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Payoneer products.

Here's a cross-join query you could run:

Cross-joins are useful for generating all possible combinations, but they can also create huge tables if you're not careful. For instance, if you had 10,000 potential customers and Payoneer had 500 different product SKUs, the resulting cross-join would have 5 million rows!

### SQL Question 4: Analyzing Transactions in Payoneer

Payoneer is an international financial services company that offers online money transfer and digital payment services. Suppose you are given a task to understand the transactional behavior of users in a given financial quarter.

Given two data tables, and , with these features:

users
id
name
email
country
registration_date
transactions
transaction_id
user_id
product_type
transaction_date
amount
status

You're asked to come up with the total number of successful transactions for each product_type in every country during the financial quarter.

##### Example Input:
idnameemailcountryregistration_date
1John Doejohndoe@gmail.comUSA01/05/2021
3Helen Brownhelenbrown@gmail.comUK11/10/2021
##### Example Input:
transaction_iduser_idproduct_typetransaction_dateamountstatus
1011CASH_TRANSFER01/07/2022\$100.00SUCCESS
1022CARD_PAYMENT03/07/2022\$78.56SUCCESS
1031CARD_PAYMENT07/08/2022\$55.40SUCCESS
1043CASH_TRANSFER05/09/2022\$75.00PENDING
1052CASH_TRANSFER02/10/2022\$210.75SUCCESS

Assuming the financial quarter is from July to September, you'd use below PostgreSQL query to find the total number of successful transactions for each product_type in every country:

This query joins the and table based on . It then filters out the transactions based on status and transaction_date range. Post that, it groups the transactions by country and product_type to get the total number of transactions. The results are ordered in descending order of total transactions.

### SQL Question 5: Can you describe the different types of joins in SQL?

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 Payoneer orders and Payoneer customers.

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

2. LEFT JOIN: A 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 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 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: Filtering Payoneer Customers by Transactions and Account Status

Consider a situation where Payoneer, a global payment company, wants to analyze their customer's transaction history to find and support their most active users while also considering their account status. Specifically, they want to find active users who have made more than ten transactions and whose accounts are verified and active.

Below are the sample database tables:

##### Example Input:

To find the desired active and verified users with more than 10 transactions, we can use a combination of SQL JOIN, WHERE, GROUP BY, and HAVING clauses. Below is the SQL query:

This query combines data from the and tables using a JOIN operation, ensuring that only customer records with a corresponding transaction are considered. The WHERE clause filters this combined data down to only those records where the account is active and verified. It then groups the data by customer_id and counts the number of transactions each customer made. The HAVING clause is used to further filter results after grouping, in this case, selecting only those customers who made more than 10 transactions.

Note: The is assumed to be unique so it is used for counting the number of transactions.

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

The UNION operator combines the results from multiple SELECT statements into a single result set.

Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of Payoneer's Facebook ads and their Google ads:

This statement would return a combined result set of both Facebook and Google ads that have a spend greater than \$200. The result set would include the following columns: ad_id, ad_name, ad_type, impressions, clicks, and spend.

Note that the two SELECT statements within the UNION must have the same number of columns and the columns must have similar data types. In this example, both SELECT statements have the same number of columns and the data types of the columns are all compatible.

### SQL Question 8: Filter Customers Based On Email Domain

As a major player in the FinTech industry, Payoneer has customers worldwide. The marketing department wants to perform a targeted email campaign based on the customer's email domain. Your task is to come up with a list of customers who use Gmail for their correspondence with Payoneer.

##### Example Input:
customer_IDFirst_nameLast_nameEmailCountry
C1001JohnDoejohn.doe@gmail.comUSA
C1003EmmaBrownemma.brown@gmail.comUK
C1004OliverJohnsonoliver.johnson@hotmail.comGermany
C1005WilliamWilliamswilliam.williams@gmail.comAustralia
##### Example Output:
customer_IDFirst_nameLast_nameEmail
C1001JohnDoejohn.doe@gmail.com
C1003EmmaBrownemma.brown@gmail.com
C1005WilliamWilliamswilliam.williams@gmail.com

In this SQL query, we are selecting the columns to be displayed in the result (customer_ID, First_name, Last_name, Email). The clause combined with the keyword is used to filter the records. The sign is used to define wildcards (it can represent zero, one or multiple characters). The underscore sign in LIKE operator can be used to define one single number or character. Here, we are looking for all emails which ends with '@gmail.com'.

### SQL Question 9: Analyze Customer Transaction Data

As a data analyst at Payoneer, your manager has rotated several teams responsible for handling customers from different regions. As a result of this rotation, some customers were handled by different teams over time. Your task is to write a SQL query that will identify all customers who were handled by more than one team.

The customer database contains two tables: Customers and Transactions. The Customers table maintains a record of customer details. The Transactions table keeps track of every transaction along with the team_id of the team responsible for that transaction at that time.

##### Example Input:
customer_idcustomer_namecustomer_email
123John Doejohn.doe@gmail.com
124Jane Doejane.doe@gmail.com
125Tom Smithtom.smith@gmail.com
##### Example Input:
transaction_idcustomer_idtransaction_dateteam_idamount
112310/08/2021 00:00:00101200
212311/08/2021 00:00:00102250
312412/08/2021 00:00:00101300
412512/08/2021 00:00:00103400
512513/08/2021 00:00:00103500

This SQL block joins the Customers and Transactions tables using the customer_id field. It groups the results by customer id, name, and email while counting the distinct number of teams for each customer. The HAVING clause is uses to filter out customers who were handled by one team, leaving only customers handled by more than one team.

Because joins come up routinely during SQL interviews, practice this Snapchat SQL Interview question using JOINS:

### SQL Question 10: In SQL, are values same the same as zero or a blank space?

A NULL value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values. It is important to handle NULL values properly in SQL because they can cause unexpected results if not treated correctly.

### Preparing For The Payoneer SQL Interview

The best way to prepare for a Payoneer SQL interview is to practice, practice, practice. Besides solving the above Payoneer SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.

Each problem on DataLemur has multiple hints, detailed solutions and most importantly, there's an interactive SQL code editor so you can instantly run your query and have it graded.

To prep for the Payoneer SQL interview you can also be a great idea to solve SQL problems from other tech companies like:

But if your SQL query skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Analytics.

This tutorial covers things like filtering strings based on patterns and handling missing data (NULLs) – both of which come up frequently during Payoneer interviews.

### Payoneer Data Science Interview Tips

#### What Do Payoneer Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems to prepare for the Payoneer Data Science Interview are:

• Probability & Statistics Questions
• Python or R Programming Questions
• Product Data Science Interview Questions
• Machine Learning and Predictive Modeling Questions
• Behavioral Based Interview Questions

#### How To Prepare for Payoneer Data Science Interviews?

To prepare for Payoneer Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions sourced from Google, Microsoft & tech startups
• a crash course on SQL, Product-Sense & ML
• over 900+ reviews on Amazon & 4.5-star rating