8 S&P Global SQL Interview Questions (Updated 2024)
S&P Global employees use SQL to analyze financial statement data, such as income statements and balance sheets, as well as to create data models that predict stock market trends and identify investment opportunities. This is the reason why S&P Global includesSQL questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
To help you practice for the S&P Global SQL interview, we've collected 8 S&P Global SQL interview questions – can you solve them?
8 S&P Global SQL Interview Questions
SQL Question 1: Identify S&P Global's Most Important Customers
S&P Global is a company that offers information services such as financial research and analysis. For this question, the "important activities" will be the purchasing of research reports and data packages. "Very Frequently" will mean at least once per week.
Assume that an important customer, or a "whale", is a customer who has spent more than $10,000 on research reports and data packages over the past 30 days. S&P Global would like to identify these customers in an effort to provide them with exceptional customer service and value-added offerings.
You are given two tables - and .
contains details of their customers and contains details of all sales transactions performed.
Write a SQL query to list down all the 'whale' customers along with their total spending in the past 30 days.
Sample Input
| customer_id | first_name | last_name | |
|---|---|---|---|
| 1 | John | Doe | john.doe@gmail.com |
| 2 | Jane | Smith | jane.smith@gmail.com |
| 3 | Jim | Brown | jim.brown@gmail.com |
| 4 | Alice | Johnson | alice.johnson@gmail.com |
| 5 | Bob | Williams | bob.williams@gmail.com |
Sample Input
| transaction_id | customer_id | transaction_date | amount |
|---|---|---|---|
| 1001 | 1 | 06/01/2022 12:00:00 | 5000 |
| 1002 | 1 | 06/20/2022 15:00:00 | 6000 |
| 1003 | 2 | 06/08/2022 13:30:00 | 9000 |
| 1004 | 3 | 06/20/2022 10:00:00 | 4000 |
| 1005 | 4 | 06/15/2022 16:00:00 | 3000 |
| 1006 | 4 | 07/20/2022 19:00:00 | 7500 |
Answer:
This query first joins the table with the table on the . This query then only includes transactions that occurred within the last 30 days. The clause groups the data by and the clause ensures that only customers who spent more than $10,000 are included in the result set. The final result is a list all the 'whale' customers along with their total spending in the past 30 days.
To work on a similar customer analytics SQL question where you can code right in the browser and have your SQL query automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Well Paid Employees
Given a table of S&P Global employee salary data, write a SQL query to find all employees who earn more than their own manager.
S&P Global 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 interview question interactively 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 solution above is confusing, you can find a detailed solution here: Employees Earning More Than Managers.
SQL Question 3: Can you provide an example of two entities that have a one-to-one relationship, and another example of a one-to-many relationship?
When designing a database schema, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a car and a license plate - each car has one license plate, and each license plate belongs to one car.
On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. For example, a person can have multiple email addresses, but each email address only relates back to one person.
SQL Question 4: Analyzing Financial Data with Window Functions
S&P Global collects various financial datasets for analysis. A recorded dataset includes daily stock market performance for a few select companies. Let's consider company 'X', 'Y', and 'Z'. You must write a PostgreSQL query that calculates the seven-day rolling average of closing prices for each company, but excludes the current day's closing price in this calculation.
The dataset is housed in the table formatted as follows:
Example Input:
| date | company | closing_price |
|---|---|---|
| 2022-01-01 | X | 1000 |
| 2022-01-02 | X | 1050 |
| 2022-01-03 | X | 1020 |
| 2022-01-04 | X | 1040 |
| 2022-01-05 | X | 1060 |
| 2022-01-06 | X | 1030 |
| 2022-01-07 | X | 1045 |
| 2022-01-08 | X | 1070 |
This data wants to be transformed into the following table:
Example Output:
| date | company | closing_price | rolling_avg_price |
|---|---|---|---|
| 2022-01-01 | X | 1000 | NULL |
| 2022-01-02 | X | 1050 | NULL |
| 2022-01-03 | X | 1020 | NULL |
| 2022-01-04 | X | 1040 | NULL |
| 2022-01-05 | X | 1060 | NULL |
| 2022-01-06 | X | 1030 | NULL |
| 2022-01-07 | X | 1045 | 1000 |
| 2022-01-08 | X | 1070 | 1025 |
Answer:
You can achieve this goal with window functions using the function and clause:
This query calculates the average closing price over the previous seven days (excluding the current day) for each company. The clause specifies that only the six previous days and the day before are included in the window for the current row. The clause specifies that the window's ordering is based on the date, and the clause resets the window for each company.
To solve a related window function SQL problem on DataLemur's free interactive coding environment, solve this Google SQL Interview Question:
SQL Question 5: Can you explain the distinction between a left and right join?
A join in SQL allows you to retrieve data from multiple tables and combine it into a single set of results.
To demonstrate the difference between left vs. right join, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: 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.
SQL Question 6: Filter Customers Based on Name Pattern
Given the customer database of S&P Global, your task is to find all customers whose first names begin with 'J' and last names end with 'son'.
Example Input:
| customer_id | first_name | last_name | account_open_date | account_type |
|---|---|---|---|---|
| 2176 | John | Jacobson | 02/08/2015 00:00:00 | Platinum |
| 9842 | Jane | Johnson | 05/10/2018 00:00:00 | Gold |
| 3921 | Jack | Robinson | 11/18/2020 00:00:00 | Silver |
| 6254 | Jerry | Samson | 07/26/2016 00:00:00 | Bronze |
| 7456 | Jessica | Thompson | 12/05/2017 00:00:00 | Platinum |
Answer:
This query uses the LIKE keyword in SQL to filter and find those records in the 'customers' database at S&P Global which match the specific pattern we are looking for. The '%' acts as a wildcard character, so 'J%' means any string that starts with 'J' and '%son' means any string that ends with 'son'. Thus, it will return a table with the customers whose names match these conditions.
SQL Question 7: What's the main difference between the 'BETWEEN' and 'IN' operators in SQL?
The operator is used to select rows that fall within a certain range of values, while the operator is used to select rows that match values in a specified list.
For example, suppose you are a data analyst at S&P Global and have a table of advertising campaign data. To find campaigns with a spend between 5k, you could use BETWEEN:
To find advertising campaigns that were video and image based (as opposed to text or billboard ads), you could use the operator:
SQL Question 8: Compute Adjusted Stock Prices Using Math Functions
S&P Global tracks a variety of financial securities. At S&P Global, we recently added an "adjustment factor" to our database to account for events like stock splits. Write a SQL query to calculate the adjusted price for each stock by multiplying the original price by the adjustment factor.
Round this adjusted price to 2 decimal places. Also, if the adjusted price has a remainder greater than 0.5 after dividing by 1, use the CEIL() function to round up the price to the next highest integer. If the remainder is less or equal to 0.5, use the FLOOR() function instead.
Please consider the following tables for this exercise:
Sample Input:
| stock_id | price | adjustment_factor |
|---|---|---|
| 101 | 100.50 | 1.5 |
| 102 | 150.75 | 0.8 |
| 103 | 200.25 | 1.2 |
| 104 | 250.00 | 0.7 |
Expected Output:
| stock_id | adjusted_price |
|---|---|
| 101 | 151 |
| 102 | 121 |
| 103 | 241 |
| 104 | 175 |
Answer:
In PostgreSQL syntax, the query is as follows:
This SQL query works by first adjusting the price by the adjustment factor and rounding the result to 2 decimal places. Then it checks whether this adjusted price has a remainder greater than 0.5 when divided by 1. If it does, the query rounds up the price to the next highest integer using the CEIL() function. If not, it uses FLOOR() function to round down the price.
To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for requiring rounding to 2 decimal places or this Alibaba Compressed Mean Question which is similar for requiring calculation of a mean and rounding.
How To Prepare for the S&P Global 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 earlier S&P Global SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Microsoft, Google, and Meta.
Each problem on DataLemur has hints to guide you, step-by-step solutions and crucially, there's an online SQL coding environment so you can right online code up your query and have it checked.
To prep for the S&P Global SQL interview you can also be useful to practice SQL problems from other financial services companies like:
Learn how S&P Global's AI solutions can help you make more informed investment decisions!
However, if your SQL query skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.
This tutorial covers SQL concepts such as aggregate window functions and UNION vs. joins – both of which pop up often in SQL interviews at S&P Global.
S&P Global Data Science Interview Tips
What Do S&P Global Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions tested in the S&P Global Data Science Interview are:
- Statistics and AB Testing Questions
- Coding Questions in Python or R
- Product-Sense Questions
- ML Modelling Questions
- Behavioral Interview Questions based on S&P Global company values\
How To Prepare for S&P Global Data Science Interviews?
To prepare for S&P Global Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG (FB, Apple, Amazon, Netflix, Google)
- a crash course on SQL, Product-Sense & ML
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