10 Synchrony Financial SQL Interview Questions (Updated 2024)
At Synchrony Financial, SQL does the heavy lifting for analyzing financial databases for insights and developing predictive models for customer behavior. That's why Synchrony Financial almost always asks SQL problems in interviews for Data Science, Analytics, and & Data Engineering jobs.
To help you practice for the Synchrony Financial SQL interview, we'll cover 10 Synchrony Financial SQL interview questions in this blog.
10 Synchrony Financial SQL Interview Questions
SQL Question 1: Identify High-Value Customers at Synchrony Financial
Working at Synchrony Financial, one important category of users could be those who frequently interact with our financial services, whether it be by using credit services, making payments, or regularly accessing their financial accounts. We want to identify these power users who interact with our services more than 10 times in a month.
Create a table called that has the following columns:
- (unique ID for each interaction)
- (unique ID for each customer)
- (date of when the interaction occurred)
- (unique ID for the type of service being used)
Your task is to write a SQL query to find those customers who had more than 10 interactions in any one month. The result should include the customer_id and the number of interactions, and the output should be sorted by the number of interactions in descending order.
Example Input:
| interaction_id | customer_id | interaction_date | service_id |
|---|---|---|---|
| 1 | 200 | 06/01/2022 | 100 |
| 2 | 200 | 06/02/2022 | 200 |
| 3 | 300 | 06/08/2022 | 200 |
| 4 | 400 | 06/14/2022 | 100 |
| 5 | 200 | 06/18/2022 | 300 |
| 6 | 200 | 06/21/2022 | 100 |
| 7 | 300 | 06/28/2022 | 100 |
| 8 | 200 | 07/01/2022 | 100 |
| 9 | 200 | 07/05/2022 | 200 |
| 10 | 200 | 07/08/2022 | 200 |
| 11 | 200 | 07/13/2022 | 300 |
| 12 | 200 | 07/15/2022 | 200 |
| 13 | 200 | 07/18/2022 | 100 |
| 14 | 200 | 07/24/2022 | 300 |
| 15 | 200 | 07/28/2022 | 200 |
Answer:
This query first groups the interactions by customer and by month, and then counts the number of interactions for each group. The resulting groups are filtered by having more than 10 interactions, and then sorted by the number of interactions in descending order, to show the customers with the most frequent interactions at the top.
To practice a similar customer analytics SQL question where you can solve it interactively and have your SQL solution instantly executed, try this Walmart Labs SQL Interview Question:
SQL Question 2: Highly-Paid Employees
Assume there was a table of Synchrony Financial employee salaries. Write a SQL query to find all employees who earn more than their own manager.
Synchrony Financial Example Input:
| 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 |
Example Output:
| 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.
Solve this question directly within the browser on DataLemur:
Answer:
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 code above is tough, you can find a detailed solution here: Employees Earning More Than Managers.
SQL Question 3: What's a constraint in SQL, and do you have any examples?
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
Say you were storing sales analytyics data from Synchrony Financial's CRM inside a database. Here's some example constraints you could use:
-
PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.
-
FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.
-
NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.
-
UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.
-
CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.
-
DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"
SQL Question 4: Analyzing Customers' Credit Card Transactions
Synchrony Financial provides funding, card-issuing, and related services to businesses. You work in the data analysis department and you're asked to provide insights on customers' credit card transactions.
The database includes a table named , each row in the table represents a credit card transaction. Each record has a unique identifier .Each customer has a unique and each credit card has a unique . The field represents the amount of the transaction and is the date of the transaction.
The company wants to find the monthly spending of each customer. Specifically, they want to find the total amount of transactions made by each customer in each month.
Example Input:
| transaction_id | customer_id | transaction_date | card_id | amount |
|---|---|---|---|---|
| 43457 | 771 | 06/02/2021 00:00:00 | 30051 | 500.00 |
| 56982 | 333 | 06/15/2021 00:00:00 | 20052 | 250.00 |
| 53293 | 771 | 07/25/2021 00:00:00 | 30051 | 125.00 |
| 86352 | 333 | 07/05/2021 00:00:00 | 20052 | 375.00 |
| 46179 | 771 | 08/04/2021 00:00:00 | 30051 | 1000.00 |
Example Details of the Output:
| mth | customer_id | total_amount |
|---|---|---|
| 6 | 771 | 500.00 |
| 6 | 333 | 250.00 |
| 7 | 771 | 125.00 |
| 7 | 333 | 375.00 |
| 8 | 771 | 1000.00 |
Answer:
This query results in a table with each row representing each customer's total transactions for each month. The WINDOW function SUM() OVER (PARTITION BY customer_id, DATE_TRUNC('month', transaction_date) is used to calculate the total transaction for each customer for each month. Output is ordered by customer_id and month for better readability.
For more window function practice, try this Uber SQL Interview Question within DataLemur's interactive coding environment:
SQL Question 5: What is the difference between a correlated subquery and non-correlated subquery?
A correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.
An example correlated sub-query:
This correlated subquery retrieves the names and salaries of Synchrony Financial 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:
This non-correlated subquery retrieves the names and salaries of Synchrony Financial employees who make more than the average salary for the Data Analytics department (which honestly should be very few people since Data Analysts are awesome).
Anways, the subquery is independent of the main query and can be considered a standalone query. Its output (the average salary for the Data Analytics department) is then used in the main query to filter the rows of the table.
SQL Question 6: Retrieve Specific Customer Credit Records
Synchrony Financial is a major issuer of credit cards for stores and other businesses. People receive various credit limits based on their credit scores. Suppose we want to identify customers who have a credit limit of over 1000 in the last month.
Assume you have access to two tables: and .
Example Input:
| customer_id | credit_score | credit_limit |
|---|---|---|
| 1287 | 710 | $6000 |
| 2874 | 620 | $4000 |
| 5469 | 800 | $8000 |
| 8987 | 500 | $3000 |
| 0214 | 740 | $7000 |
Example Input:
| payment_id | customer_id | payment_date | payment_amount |
|---|---|---|---|
| 9921 | 1287 | 05/20/2022 | $2000 |
| 7584 | 2874 | 06/01/2022 | $500 |
| 8259 | 5469 | 06/10/2022 | $1500 |
| 2648 | 8987 | 06/15/2022 | $800 |
| 5127 | 0214 | 06/20/2022 | $1200 |
We want to retrieve a table with customer_id, credit_limit, and largest payment in the last month.
Example Output:
| customer_id | credit_limit | largest_payment_last_month |
|---|---|---|
| 1287 | $6000 | $2000 |
| 5469 | $8000 | $1500 |
| 0214 | $7000 | $1200 |
Answer:
This SQL query joins the and tables on and filters based on the conditions given. It then aggregates by and , finding the maximum payment made in the past month for each of these customer groups.
SQL Question 7: Describe the difference between UNION and UNION ALL.
Both and are used to combine the results of two or more SELECT statements into a single result set.
However, only includes one instance of a duplicate, whereas includes duplicates.
Check out the Synchrony Data & Analytics team and see how they use SQL in their day-today operations.
SQL Question 8: Calculate the Average Loan Amounts
As the data analyst at Synchrony Financial, you are tasked with calculating the average loan amount for each type of loan products in each year. Your supervisor wants to know if there are any trends with the averages over the years which will be useful for future business decisions.
Example Input:
| loan_id | customer_id | loan_date | product_type | loan_amount |
|---|---|---|---|---|
| 101 | 1111 | 2019-11-15 | Auto | 15000 |
| 102 | 2222 | 2019-12-20 | Auto | 18000 |
| 103 | 3333 | 2020-01-10 | Mortgage | 200000 |
| 104 | 4444 | 2020-02-25 | Mortgage | 210000 |
| 105 | 5555 | 2020-05-05 | Personal | 5000 |
Example Output:
| year | product_type | avg_loan_amount |
|---|---|---|
| 2019 | Auto | 16500 |
| 2020 | Mortgage | 205000 |
| 2020 | Personal | 5000 |
Answer:
In the given SQL query, we are extracting the year from the loan_date using PostgreSQL's function. Then we group the data based on the extracted year and product_type, and calculate the average loan amount for each group. The result is ordered in ascending order of year and product_type.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating year-on-year rates or this Amazon Average Review Ratings Question which is similar for calculating averages of a variable.
SQL Question 9: Calculating Click-Through Rates for Synchrony Financial
Synchrony Financial is interested in understanding their clients' behavior on their website. They have two main actions of interest: views of product pages from their financial services, and the actual addition of these products into the user's cart.
Given data from the actions on the website, can you find the click-through rates (CTR) of each financial product? The click-through rate is defined as the number of times a product was added to the cart divided by the number of times the product page was viewed.
The provided tables are and :
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 101 | 456 | 06/08/2022 00:00:00 | 20001 |
| 102 | 789 | 06/10/2022 00:00:00 | 30002 |
| 103 | 456 | 06/18/2022 00:00:00 | 20001 |
| 104 | 123 | 07/26/2022 00:00:00 | 10001 |
| 105 | 789 | 07/05/2022 00:00:00 | 10001 |
Example Input:
| activity_id | user_id | activity_date | product_id |
|---|---|---|---|
| 201 | 456 | 06/08/2022 00:01:00 | 20001 |
| 202 | 456 | 06/11/2022 00:00:00 | 20001 |
| 203 | 123 | 07/26/2022 00:02:00 | 10001 |
Answer:
The solution can be obtained by grouping each table by product_id, counting number of rows in each group, and then joining the two tables and dividing the counts. The SQL query would look as follows in PostgreSQL:
This script will return the click-through rate of each product by dividing the number of times the product was added to the cart by the number of times the product page was viewed. If a product was never added to the cart, '0' is returned as the click-through rate.
To solve a similar problem about calculating rates, try this TikTok SQL Interview Question within DataLemur's online SQL coding environment:
SQL Question 10: What's the purpose of the constraint?
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
Say for example you had sales analytics data from Synchrony Financial's CRM (customer-relationship management) tool.
In this example, the table has a foreign key field called that references the "account_id" field in the table (the primary key). This helps to link the data about each opportunity to the corresponding account information in the accounts table.
This makes sure the insertion of rows in the table that do not have corresponding entries in the table. It also helps to enforce the relationship between the two tables and ensures that data is not deleted from the table if there are still references to it in the table.
Synchrony Financial SQL Interview Tips
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.
Besides solving the above Synchrony Financial SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.
Each problem on DataLemur has multiple hints, detailed solutions and most importantly, there's an online SQL coding environment so you can instantly run your SQL query and have it executed.
To prep for the Synchrony Financial SQL interview you can also be useful to practice SQL problems from other banking & finanacial services companies like:
In case your SQL query skills are weak, forget about diving straight into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers SQL topics like cleaning text data and math functions like CEIL()/FLOOR() – both of which come up often during Synchrony Financial interviews.
Synchrony Financial Data Science Interview Tips
What Do Synchrony Financial Data Science Interviews Cover?
In addition to SQL interview questions, the other question categories to practice for the Synchrony Financial Data Science Interview are:
- Statistics & Probability Questions
- Python Pandas or R Coding Questions
- Product Analytics Questions
- ML Modelling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Synchrony Financial Data Science Interviews?
I'm sorta biased, but I believe the optimal way to prepare for Synchrony Financial Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
The book covers 201 data interview questions taken from FAANG, tech startups, and Wall Street. It also has a crash course covering Stats, SQL & ML. And finally it's helped a TON of people, which is why it's got over 1000+ 5-star reviews on Amazon.