Data Science, Data Engineering, and Data Analytics employees at Point72 uses SQL to analyze large financial datasets, including stock prices, trading volumes, and market sentiment, to identify trends and patterns that inform trading strategies. It is also used to manage large databases, such as those containing portfolio performance and risk metrics, for optimal information retrieval, which is why Point72 asks SQL questions during job interviews.
As such, to help you ace the Point72 SQL interview, we've curated 8 Point72 SQL interview questions – scroll down to start solving them!
Point72 wants to identify its most valuable clients, Given the table that tracks the number of trades, and the from each trade, write a SQL query to return the top 5 clients with the highest total profit in the last 12 months.
Sample data:
trade_id | client_id | trade_date | profit |
---|---|---|---|
2203 | 561 | 15/10/2021 | 5000 |
4301 | 152 | 10/11/2021 | 4000 |
1501 | 348 | 05/01/2022 | 5500 |
9823 | 561 | 25/06/2022 | 6500 |
8402 | 561 | 15/08/2022 | 6000 |
3456 | 348 | 05/09/2021 | 3000 |
This SQL solution first filters the trades that took place in the last twelve months. Then it groups trades by client_id and calculates the total profit for each client. Finally, it orders the results in descending order of total profit and returns the top 5 clients.
To work on a similar customer analytics SQL question where you can solve it interactively and have your SQL solution automatically checked, try this Walmart SQL Interview Question:
You're given a table of Point72 employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
You can solve this question directly within the browser on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.
Constraints are just rules your DBMS has to follow when updating/inserting/deleting data.
Say you had a table of Point72 products and a table of Point72 customers. Here's some example SQL constraints you'd use:
NOT NULL: This constraint could be used to ensure that certain columns in the product and customer tables, such as the product name and customer email address, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the product IDs and customer IDs are unique. This would prevent duplicate entries in the respective tables.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for each table. The product ID or customer ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the Point72 product and customer tables. For example, you could use a foreign key to link the customer ID in the customer table to the customer ID in the product table to track which products each customer has purchased.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that Point72 product prices are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the customer registration date to the current date if no value is provided when a new customer is added to the database.
As an analyst at Point72, you have access to an extensive financial transaction database. One key metric often reviewed in the company is the monthly average trade volume for each security. You are tasked to write a SQL query that will calculate the monthly average trade volume for each security in the 'transactions' table.
transaction_id | security_id | trade_date | volume |
---|---|---|---|
1234 | SPY | 01/10/2022 00:00:00 | 5000 |
1567 | AAPL | 01/11/2022 00:00:00 | 9500 |
1842 | AAPL | 01/15/2022 00:00:00 | 3000 |
7421 | TSLA | 02/21/2022 00:00:00 | 12000 |
5917 | SPY | 02/25/2022 00:00:00 | 5500 |
4157 | TSLA | 01/27/2022 00:00:00 | 18000 |
With this query, we first group the data by security and the month of the trade date (). We then calculate the average volume of this partition using the function. The result is a table reporting the monthly average trade volume for each security.
mth | security | avg_volume |
---|---|---|
1 | AAPL | 6250 |
1 | SPY | 5000 |
1 | TSLA | 18000 |
2 | SPY | 5500 |
2 | TSLA | 12000 |
To practice a similar window function question on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:
A self-join is a operation in which a single table is joined to itself. To perform a self-join, you must specify the table name twice in the clause, giving each instance a different alias. You can then join the two instances of the table using a clause, and specify the relationship between the rows in a clause.
Think of using a self-joins whenever your data analysis involves analyzie pairs of the same things, like comparing the salaries of employees within the same department, or identifying pairs of products that are frequently purchased together (which you can do in this real SQL question from a Walmart interview).
For another self-join example, suppose you were conducting an HR analytics project and needed to examine how frequently employees within the same department at Point72 interact with one another, you could use a self-join query like the following to retrieve all pairs of Point72 employees who work in the same department:
This query returns all pairs of Point72 employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Point72 employee being paired with themselves).
Point72 has a database of all customer transactions. The transactions table records the customer id, transaction date, transaction amount, and product id for each transaction.
Write an SQL query to filter down this transactions data to only include records where the transaction amount is more than $1,000 and the transaction took place in the year 2022.
Use the provided sample tables to test your query.
transaction_id | customer_id | transaction_date | amount | product_id |
---|---|---|---|---|
1 | 665 | 01/03/2022 00:00:00 | 1500 | 201 |
2 | 432 | 06/09/2021 00:00:00 | 2000 | 202 |
3 | 772 | 04/11/2022 00:00:00 | 500 | 203 |
4 | 232 | 07/22/2022 00:00:00 | 2000 | 204 |
5 | 994 | 08/05/2022 00:00:00 | 500 | 205 |
transaction_id | customer_id | transaction_date | amount | product_id |
---|---|---|---|---|
1 | 665 | 01/03/2022 00:00:00 | 1500 | 201 |
4 | 232 | 07/22/2022 00:00:00 | 2000 | 204 |
This query first filters the transactions table for rows where the transaction amount is more than $1,000. It then further filters those records to include only the ones where the transaction date is in the year 2022.
"The clause in SQL allows you to select records that are unique, eliminating duplicates.
For a tangible example, say you had a table of Point72 employees:
first_name | job_title |
---|---|
Akash | Data Analyst |
Brittany | Data Scientist |
Carlos | Data Engineer |
Diego | Data Engineer |
Eva | Data Analyst |
If you were doing an HR Analytics project and you wanted to get all the unique job titles that currently worked at the company, you would write the following SQL query:
The output would give you 3 distinct job titles at Point72:
job_title |
---|
Data Analyst |
Data Scientist |
Data Engineer |
As a financial company, Point72 deals with multiple stocks. Your task is to find the average trading volume for each stock.
trade_id | trade_date | stock_id | traded_volume |
---|---|---|---|
101 | 2022-07-01 | S01 | 5000 |
102 | 2022-07-02 | S01 | 3000 |
103 | 2022-07-03 | S02 | 4500 |
104 | 2022-07-04 | S02 | 4000 |
105 | 2022-07-05 | S01 | 5500 |
106 | 2022-07-06 | S02 | 4200 |
stock | avg_trading_volume |
---|---|
S01 | 4500 |
S02 | 4233.33 |
With this query, we group by the and then calculate the average trading for each . These will allow us to get the desired result, which is the average trading volume for each stock. Please note that the output for the average is truncated for readability.
To practice a very similar question try this interactive Robinhood Cities With Completed Trades Question which is similar for requiring querying trade data or this Stripe Repeated Payments Question which is similar for needing aggregated analysis of transaction data.
The best way to prepare for a Point72 SQL interview is to practice, practice, practice. Besides solving the above Point72 SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like FAANG tech companies and tech startups.
Each interview question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there's an online SQL code editor so you can easily right in the browser your SQL query answer and have it checked.
To prep for the Point72 SQL interview it is also helpful to practice SQL problems from other finance companies like:
Dive into the world of Artificial Intelligence and Machine Learning with Point72's expert insights and innovative approaches!
In case your SQL coding skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers things like HAVING and filtering data with boolean operators – both of which come up routinely during Point72 SQL assessments.
In addition to SQL query questions, the other types of questions to prepare for the Point72 Data Science Interview are:
To prepare for Point72 Data Science interviews read the book Ace the Data Science Interview because it's got:
Don't forget about the behavioral interview – prepare for that with this guide on behavioral interview questions.