At Jackson Financial, SQL used for analyzing financial data for trend predictions, and managing client databases for personalized financial solutions. That's why Jackson Financial frequently asks SQL coding questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, to help you prep, here's 9 Jackson Financial SQL interview questions – able to solve them?
As a data analyst for Jackson Financial, your task is to identify the VIP customers. VIP customers are defined as those who make at least 5 transactions amounting to more than 10000 dollars per month on average. Use the customer and transactions tables in the database to find these customers.
customer_id | first_name | last_name |
---|---|---|
1 | John | Doe |
2 | Jane | Smith |
3 | Oliver | Queen |
4 | Bruce | Wayne |
5 | Clark | Kent |
transaction_id | customer_id | transaction_date | amount |
---|---|---|---|
101 | 1 | 2022-06-05 | 3000 |
102 | 1 | 2022-06-10 | 2500 |
103 | 1 | 2022-06-20 | 5000 |
104 | 1 | 2022-07-01 | 4000 |
105 | 2 | 2022-06-15 | 7000 |
106 | 2 | 2022-06-25 | 5000 |
107 | 3 | 2022-06-05 | 2000 |
108 | 3 | 2022-06-19 | 3000 |
109 | 3 | 2022-07-30 | 1500 |
110 | 4 | 2022-06-07 | 3500 |
111 | 5 | 2022-07-15 | 4500 |
This query firstly groups the transactions by the customer and the month & year of transaction. It counts the number of transactions and calculates total amount for those transactions. Then it joins this result with the customers table to get the names of the customers. The last filter is applied to only show those customers who have made more than 5 transactions and the total amount is more than 10000 in a month. This helps to identify the VIP customers.
To work on a similar customer analytics SQL question where you can solve it right in the browser and have your SQL code automatically checked, try this Walmart SQL Interview Question:
Given a table of Jackson Financial employee salary data, write a SQL query to find the 2nd highest salary amongst all the employees.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Test your SQL query for this question and run your code right in DataLemur's online SQL environment:
You can find a detailed solution with hints here: 2nd Highest Salary.
The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
For example, say you were analyzing salaries for analytics employees at Jackson Financial:
This query retrieves the total salary for each Analytics department at Jackson Financial and groups the rows by the specific department (i.e. "Marketing Analytics", "Business Analytics", "Sales Analytics" teams).
The clause then filters the groups to include only Jackson Financial departments where the total salary is greater than $1 million
At Jackson Financial, you have been provided with a table of transactions which includes details of the transaction ID, the customer ID, the product ID, the amount of the transactions, and the date when the transactions took place.
The question is:
Write a SQL query to determine the average amount of transactions per customer and the rank of each transaction (for each customer) based on the transaction amount in descending order.
You can assume the transactions are unique by ID for each customer.
Here are sample input and output examples:
transaction_id | customer_id | product_id | amount | transaction_date |
---|---|---|---|---|
1001 | 2001 | 3001 | 500 | 2022-01-01 |
1002 | 2001 | 3002 | 800 | 2022-01-02 |
1003 | 2002 | 3001 | 600 | 2022-01-03 |
1004 | 2002 | 3002 | 1200 | 2022-01-04 |
1005 | 2003 | 3001 | 700 | 2022-01-05 |
customer_id | average_amount | transaction_id | transaction_rank |
---|---|---|---|
2001 | 650.00 | 1001 | 2 |
2001 | 650.00 | 1002 | 1 |
2002 | 900.00 | 1003 | 2 |
2002 | 900.00 | 1004 | 1 |
2003 | 700.00 | 1005 | 1 |
In this solution, we use the window (or analytic) function to calculate the average transaction amount per customer. The function is used to assign a rank to each transaction within each customer based on the transaction amount in descending order. We use to create a partition of rows with the same . is used to sort the results by and .
For more window function practice, solve this Uber SQL Interview Question within DataLemur's online SQL code editor:
As the name implies, the window function ranks each row within your window/partition. If two rows have the same rank, the next number in the ranking will be the previous rank plus the number of duplicates. For example, if you've got three records tied for 5th place, they'll get the values 5, 6, and 7, and the next row after this 3-way tie will have a rank of 8.
The function assigns a distinct rank to each row within a partition based on the provided column value with no gaps. This function will assign the same rank to two rows if they have the same rank, and give the next row the next rank number. To make this more concrete, imagine you had three records at rank 5 – then, the next rank would be 6.
Jackson Financial has been running promotional campaigns for their financial products. For this, they have been targeting potential customers with online advertisements. When a user views their ads, he/she may click through them leading to a product page on their website. Some of these page views result in the product being added to cart for purchase.
The management at Jackson Financial wants to determine the effectiveness of these campaigns. Specifically, they're interested in the click-through conversion rate -- the ratio of the number of products added to cart after viewing the ad to the total number of ad views for each product.
Given two tables and that log every instance of an ad click through and product addition to the cart respectively, write a SQL query to calculate the click-through conversion rate for each product.
ad_id | user_id | view_date | product_id |
---|---|---|---|
101 | 567 | 06/08/2021 | 201 |
102 | 890 | 06/10/2021 | 202 |
103 | 567 | 06/18/2021 | 203 |
104 | 453 | 07/26/2021 | 201 |
105 | 981 | 07/05/2021 | 202 |
cart_id | user_id | add_to_cart_date | product_id |
---|---|---|---|
901 | 567 | 06/08/2021 | 201 |
902 | 567 | 06/10/2021 | 203 |
903 | 453 | 06/18/2021 | 201 |
904 | 981 | 07/08/2021 | 202 |
905 | 981 | 07/09/2021 | 202 |
This query first joins and tables based on and . It then groups the result by and uses SQL's count function to calculate the total number of unique clicks on ads () and addition to carts (). The click-through conversion rate is obtained by dividing the number of unique additions to cart (after viewing an ad) by the total number of ad views per product.
To solve another question about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive coding environment:
Take a look at the most recent quarterly results from Jackson Financial and see how the compnay has performed over the years.
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.
As a part of customer service improvement, Jackson Financial would like to learn which customers are based in New York State. The relevant information rests in the customer address, which is stored in a single 'address' column of the 'customer' table. This column contains the full address including the city and the state.
The task is to return customer records where the 'address' contains 'New York'. The address can appear anywhere in the string - at the start, in the middle or at the end.
The 'customer' table has the following columns: customer_id (integer), first_name (string), last_name (string), and address (string).
customer_id | first_name | last_name | address |
---|---|---|---|
101 | John | Doe | 123 Main St, New York, NY, 10001 |
102 | Jane | Doe | 456 Pine St, Albany, NY, 12207 |
103 | Jim | Smith | 789 Oak St, Dallas, TX, 75201 |
104 | Jill | Smith | 321 Elm St, Buffalo, NY, 14202 |
105 | Joe | Doe | 654 Maple St, Miami, FL, 33131 |
customer_id | first_name | last_name | address |
---|---|---|---|
101 | John | Doe | 123 Main St, New York, NY, 10001 |
102 | Jane | Doe | 456 Pine St, Albany, NY, 12207 |
104 | Jill | Smith | 321 Elm St, Buffalo, NY, 14202 |
This query uses the operator in the clause to filter for rows in the 'customer' table, where the 'address' column contains the string 'New York'. Since we don't know where this string will appear within the address, we use the wildcard character '%' at both sides of 'New York'. This allows any characters to be before or after 'New York' in the address string.
Jackson Financial is a company that provides a variety of financial products to its customers. The company tracks customer transactions and customer feedback. The company would like to know which products are popular among which customer demographic based on transaction data and feedback data.
In this question, there are two tables. The first one is the table where each record signifies a transaction done by a user. The table contains customer feedback on the products they purchased.
Transaction_ID | Product_ID | Customer_ID | Transaction_Date | Transaction_Amount |
---|---|---|---|---|
1001 | 9001 | 101 | 10/01/2022 00:00:00 | 200 |
1002 | 9001 | 102 | 10/01/2022 00:00:00 | 150 |
1003 | 9002 | 101 | 10/02/2022 00:00:00 | 175 |
1004 | 9002 | 103 | 10/04/2022 00:00:00 | 250 |
1005 | 9003 | 103 | 10/03/2022 00:00:00 | 300 |
Feedback_ID | Product_ID | Customer_ID | Rating |
---|---|---|---|
7001 | 9001 | 101 | 3 |
7002 | 9001 | 102 | 4 |
7003 | 9002 | 101 | 5 |
7004 | 9002 | 103 | 2 |
7005 | 9003 | 103 | 4 |
Write a SQL query to find the average transaction amount and average rating per product.
This query first calculates the average rating for each product from the table. Then, it joins this result with the table on the column. Finally, it calculates the average transaction amount for each product. The result will be a list of products with their average transaction amount and average rating.
Because joins come up routinely during SQL interviews, try this interactive Snapchat Join SQL question:
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Jackson Financial SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Jackson Financial SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each interview question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there's an online SQL code editor so you can right online code up your query and have it executed.
To prep for the Jackson Financial SQL interview it is also a great idea to practice SQL questions from other banking & finanacial services companies like:
However, if your SQL query skills are weak, forget about going right into solving questions – improve your SQL foundations with this SQL interview tutorial.
This tutorial covers things like functions like SUM()/COUNT()/AVG() and creating pairs via SELF-JOINs – both of these show up frequently during Jackson Financial SQL interviews.
In addition to SQL interview questions, the other topics to practice for the Jackson Financial Data Science Interview are:
The best way to prepare for Jackson Financial Data Science interviews is by reading Ace the Data Science Interview. The book's got: