# 11 Two Sigma SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

Data Science, Data Engineering, and Data Analytics employees at Two Sigma write SQL queries to extract and manipulate large datasets from various databases, including financial market data, trading records, and portfolio performance metrics. They also develop complex queries for statistical analysis, such as regression analysis and risk modeling, to inform investment strategies, which is why Two Sigma often asks jobseekers SQL SQL coding interview questions.

To help you ace the Two Sigma SQL interview, here’s 11 Two Sigma SQL interview questions – scroll down to start solving them!

## 11 Two Sigma SQL Interview Questions

### SQL Question 1: Identify Top Trading Users

Two Sigma is involved in financial services and uses technology and mathematical methods to derive financial insights. For this question, we require you to analyze their hypothetical trading database. Specifically, you are required to write a SQL query to identify the top 10 users who have executed the most trades in the last 30 days.

We have made two tables for you, and . The table contains user details while the table logs all trades executed.

##### Example Input:
user_idfirst_namelast_nameregistration_date
1JohnDoe01/20/2022
2JaneSmith06/15/2022
3PeterParker05/18/2022
4HarryPotter02/12/2022
5LilyEvans02/12/2022
##### Example Input:
101109/15/202250001
102209/16/202269852
103109/17/202250001
104409/18/202269852
105309/19/202269852
106109/19/202250001

This query first joins the table with the table on . The clause is used to filter out the trades from the last 30 days. It then groups the results by , , and and calculates the trade count for each user. The results are ordered by in descending order, and finally, it limits the result to the top 10 users.

To practice a similar customer analytics SQL question where you can solve it interactively and have your SQL code automatically checked, try this Walmart Labs SQL Interview Question:

### SQL Question 2: Top Three Salaries

Given a table of Two Sigma employee salaries, write a SQL query to find the top 3 highest paid employees within each department.

#### Two Sigma Example Input:

employee_idnamesalarydepartment_id
1Emma Thompson38001
2Daniel Rodriguez22301
3Olivia Smith20001
4Noah Johnson68002
5Sophia Martinez17501
8William Davis68002
10James Anderson40001

#### Example Input:

department_iddepartment_name
1Data Analytics
2Data Science

#### Example Output:

department_namenamesalary
Data AnalyticsJames Anderson4000
Data AnalyticsEmma Thompson3800
Data AnalyticsDaniel Rodriguez2230
Data ScienceNoah Johnson6800
Data ScienceWilliam Davis6800

Write a SQL query for this problem and run your code right in DataLemur's online SQL environment:

We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.

If the code above is tough, you can find a detailed solution here: Top 3 Department Salaries.

### SQL Question 3: What does the keyword do?

"The clause in SQL allows you to select records that are unique, eliminating duplicates.

For a tangible example, say you had a table of Two Sigma employees:

first_namejob_title
AkashData Analyst
BrittanyData Scientist
CarlosData Engineer
DiegoData Engineer

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 Two Sigma:

job_title
Data Analyst
Data Scientist
Data Engineer

### SQL Question 4: Calculate Average Ratings Over Time

In a finance company like Two Sigma, financial analysts rely on a steady stream of timely and accurate data. Let's consider a hypothetical table that stores the ratings given by users to different stocks on a particular date. Each row in the table records a unique submission where a user rates a stock on a scale of 1 to 5.

Your task is to write a SQL query that calculates the average rating for each stock at the end of each month.

We will be using the for this task with the following columns:

• : Unique identifier for each rating.
• : Identity of the user who submitted the rating. Users can submit multiple ratings.
• : Date on which the rating was submitted. It's in 'MM/DD/YYYY' format.
• : Unique identifier for each stock.
• : Star rating. Can have a value from 1 to 5.

#### Example Input:

rating_iduser_idsubmit_datestock_idstars
10151605/19/202239214
20213305/15/202256125
30351605/29/202239212
40496506/01/202228123
50513306/22/202256125
60696506/25/202228125
70751607/03/202239213
80813307/31/202256124

#### Example Output:

month_yearstock_idavg_rating
05/202239213.0
05/202256125.0
06/202228124.0
06/202256125.0
07/202239213.0
07/202256124.0

This query groups the table by month-year and stock_id, then calculates the average rating for each of these groups. First, the function formats the submit_date to 'MM/YYYY'. Then, the function calculates average ratings for each group. Finally, the clause sorts the output by month_year and stock_id.

For more window function practice, try this Uber SQL problem on DataLemur's interactive coding environment:

### SQL Question 5: In SQL, are blank spaces the same as a NULL?

In SQL, zero's are numerical values which can be used in calculations and comparisons just like any other number. A blank space, also known as an empty string, is a character value and can be used in character manipulation functions and comparisons.

NULLs aren't the same as zero's or blank spaces. NULLs represent unkonwn, missing, or not applicable values. They are not included in calculations and comparisons involving NULL values always result in NULL.

### SQL Question 6: Portfolio Performance Analysis

Consider the following business problem: Two Sigma needs to analyze the performance of their managed portfolios. Each portfolio consists of multiple financial instruments like stocks, bonds, and options etc. For this analysis, they specifically want to know each portfolio's total current market value, and how much it has gained (or lost) over a specific period e.g., last 30 days.

We have two tables: and . The table has an entry for each instrument in every portfolio. The table keeps track of each instrument's current market value and its value 30 days ago.

Here is the schema for table:

##### Example Input:
idportfolio_nameinstrument_idquantity
1Growth Portfolio101100
2Growth Portfolio10250
3Balance Portfolio10370
4Income Portfolio104200
5Income Portfolio105150

and for table:

##### Example Input:
idnamecurrent_market_valuevalue_30_days_ago
102Apple Stock15001550
103IBM Stock12001220
104Tesla Bond1000980
105Intel Bond800750

Write a PostgreSQL query that calculates each portfolio's total current market value and how much it has gained (or lost) over the last 30 days.

This query joins two tables and on and respectively. Then, it groups the data by and calculates the total current market value of each portfolio and the total gain or loss over the last 30 days.

### SQL Question 7: What's denormalization, and when does it make sense to do it?

Denormalization is the process of modifying a database schema in a way that deviates from the typical rules of normalization (1NF, 2NF, 3NF, etc.). There's a few reasons to denormalize a database:

Improved performance: Joins are slow AF, especially when dealing with the massive datasets that are typically used at Two Sigma. Denormalization can improve the performance of a database by reducing the number of joins that are required to retrieve data. This can be particularly useful when the database is being used for querying and reporting purposes, as joins can be expensive and slow.

Scalability: By reducing the amount of data that needs to be read and processed to execute a query, denormalization can enhance the scalability of a database. This can be useful when the database is anticipated to handle a large number of read-only queries (such as in OLAP use cases).

Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.

Because denormalization can create more complex update and delete operations, and pose potential data integrity issues, consider denormalization only if joins are causing performance bottlenecks.

### SQL Question 8: Calculate the Average Monthly Rating for Each Product

Your task is to write an SQL query that calculates the average rating for each product on a monthly basis. The query should be writen based on the table that registers each review made by customers. Assume that the column represents the date of the review submission and it is stored in a format. The column represents the rating from 1 to 5 stars.

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
617112306/08/2022 00:00:00500014
780226506/10/2022 00:00:00698524
529336206/18/2022 00:00:00500013
635219207/26/2022 00:00:00698523
451798107/05/2022 00:00:00698522
##### Example Output:
mthproductavg_stars
6500013.50
6698524.00
7698522.50

In the above query, first we convert into date format and extract the month using function. Then, we group the records by month and and calculate the average of for each group using function. The results are ordered by the month and to make it more understandable. Please ensure that your PostgreSQL version supports these functions.

### SQL Question 9: Average Shares by Traders

At Two Sigma, which is a hedge fund, they may be interested in knowing the average number of shares traded per trading day by a trader. Your task is to write an SQL query to find the average number of shares traded per day by each trader given the following tables: and .

1John
2Alice
3Bob
##### Example Input:
100012022-07-20500
100112022-07-20200
100222022-07-20300
100322022-07-21200
100432022-07-20150
100532022-07-22250

Expected output:

##### Example Output:
John350.00
Alice250.00
Bob200.00

Here, each trader's id from the table is matched with the table to find all their trades. Then, the average number of shares for each trader is calculated. The result is a table of traders and their average number of shares traded per trading day.

To practice a very similar question try this interactive Robinhood Cities With Completed Trades Question which is similar for calculating metrics based on trading data or this Microsoft Teams Power Users Question which is similar for calculating user activity data.

### SQL Question 10: Could you describe a self-join and provide a scenario in which it would be used?

A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the FROM clause, and give each instance of the table a different alias. You can then join the two instances of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

Self-joins are the go-to technique for any data analysis that involves pairs of the same thing, like identifying pairs of products that are frequently purchased together like in this Walmart SQL interview question.

For another example, say you were doing an HR analytics project and needed to analyze how much all Two Sigma employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of Two Sigma employees who work in the same department:

This query returns all pairs of Two Sigma employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Two Sigma employee being paired with themselves).

### SQL Question 11: Filtering Customer Records by Name

Two Sigma is constantly analyzing customer data for business optimization. As part of this analysis, there's often a need to find customer records based on partial information, such as a portion of the customer's name.

In this SQL question, you're asked to filter customer data from the database table to find all customers with a name that 'starts with' a given string. The case-insensitive string filter for this task is 'SMI'.

##### Example Input:
1SmithJohnsmithjohn@example.com2019-04-242021-08-21
2JoeSmithjoe.smith@example.com2018-05-122021-09-28
3JaneDoejdoe@example.com2020-11-292021-09-15
4EmilySmithemily.smith@example.com2017-01-302021-07-09
5SamSmithesam.smithe@example.com2016-03-142021-06-12

In the provided answer, the PostgreSQL keyword is used to perform a case-insensitive search on the field in the 'customers' table. This will find all customers whose last name starts with 'SMI', regardless of case.

### How To Prepare for the Two Sigma 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. Beyond just solving the earlier Two Sigma SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Google, Microsoft and Silicon Valley startups.

Each interview question has multiple hints, full answers and most importantly, there is an online SQL coding environment so you can right online code up your SQL query and have it checked.

To prep for the Two Sigma SQL interview you can also be wise to solve interview questions from other hedge funds like:

Learn how Two Sigma is leveraging AI to solve complex problems and create new opportunities!

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

This tutorial covers SQL concepts such as math functions in SQL and filtering with LIKE – both of which come up often during SQL interviews at Two Sigma.

### Two Sigma Data Science Interview Tips

#### What Do Two Sigma Data Science Interviews Cover?

In addition to SQL interview questions, the other types of questions covered in the Two Sigma Data Science Interview are:

#### How To Prepare for Two Sigma Data Science Interviews?

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