10 Charles Schwab SQL Interview Questions (Updated 2024)
At Charles Schwab, SQL is used across the company for analyzing financial data for trend identification, and improving client database management for personalized marketing strategies. That's the reason behind why Charles Schwab LOVES to ask SQL questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, to help you prepare, here’s 10 Charles Schwab SQL interview questions – how many can you solve?
10 Charles Schwab SQL Interview Questions
SQL Question 1: Charles Schwab VIP Customer Analysis
Charles Schwab is a prominent brokerage firm that regularly hosts trades for users. You have given a task by the company to visualise the most active users, identified as individuals who carry out high volume trades (more than 100,000) in a given month. Using the customer database, can you write a SQL query to identify VIP users for the month of August 2022?
The tables 'users' and 'transactions' have the following schema:
Example Input
| user_id | name | registration_date |
|---|---|---|
| 9143 | John | 01/08/2021 |
| 8562 | Ana | 22/11/2021 |
| 1092 | Mike | 31/12/2021 |
| 5085 | Ali | 27/04/2022 |
| 2076 | Sara | 11/05/2022 |
Example Input
| transaction_id | user_id | transaction_date | volume |
|---|---|---|---|
| 7817 | 9143 | 01/08/2022 | 125000 |
| 7035 | 8562 | 06/08/2022 | 98000 |
| 9421 | 1092 | 12/08/2022 | 107000 |
| 4533 | 5085 | 19/08/2022 | 133000 |
| 2312 | 2076 | 25/08/2022 | 110000 |
Answer:
Write-up:
This query begins by joining the users' information with their corresponding transactions data. It selects only the transactions that occurred within the defined month of August 2022 with '>=2022-08-01' and '<2022-09-01'.
The query then groups transactions by different users, calculates the total trade volume per user with the aggregate function SUM, and uses the HAVING clause to filter out those users with a total trade volume of less than 100,000, which is stated as the cutoff for VIP customers.
Finally, it orders the users in a descending order based on the volume of their trades, so the highest trading users (the VIP customers) appear on top.
To practice a similar power-user data analysis problem question on DataLemur's free online SQL code editor, try this Microsoft Teams Power User SQL Interview Question:
SQL Question 2: Top 3 Department Salaries
Given a table of Charles Schwab employee salaries, write a SQL query to find the top 3 highest earning employees within each department.
Charles Schwab Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Write a SQL query for this question interactively on DataLemur:
Answer:
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 hard to understand, you can find a step-by-step solution with hints here: Top 3 Department Salaries.
SQL Question 3: What is normalization?
Database normalization is the process of breaking down a table into smaller and more specific tables and defining relationships between them via foreign keys. This minimizes redundancy, and creates a database that's more flexible, scalable, and easier to maintain. It also helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies.
SQL Question 4: Analyze Investment Portfolio Performance on Monthly Basis
Charles Schwab company, a Bank and brokerage firm, has an investment portfolio for its customers. Assume you have been given a dataset that contains information about every trade which includes the trade date, customer id, stock symbol, and the return percentage for that trade.
Your task is to write a SQL query to calculate the monthly average return and the rank of each investment in terms of its monthly return using window functions.
The sample table named is shown below.
Example Input:
| trade_id | customer_id | trade_date | stock_symbol | return_percentage |
|---|---|---|---|---|
| 1245 | 560 | 01/12/2022 00:00:00 | AAPL | 5.2 |
| 1897 | 752 | 01/20/2022 00:00:00 | GOOG | 7.1 |
| 1470 | 560 | 01/25/2022 00:00:00 | AAPL | 4.8 |
| 2653 | 871 | 01/30/2022 00:00:00 | FB | 6.5 |
| 3808 | 001 | 02/04/2022 00:00:00 | AAPL | 5.3 |
| 4621 | 753 | 02/14/2022 00:00:00 | FB | 6.4 |
The output should have a month, stock symbol, monthly average returns and the rank.
Answer:
This PostgreSQL query operates in the following way:
- It extracts the month from the trade date () to group trades by month.
- It calculates the monthly average return for each stock ().
- It then ranks each stock by its average return within each month ().
- The result is then ordered by month in ascending order and rank in ascending order, showing the best performing stock at the top for each month.
p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: What are the similarities and differences between a clustered index and non-clustered index?
Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.
SQL Interview Question 6: Filtering Charles Schwab Customers’ Data
As a Data Analyst at Charles Schwab, one of your daily tasks is to review and analyze the customer records. Your job today is to filter out all the distinct customers whose opening balance is more than $50000 and belongs to 'Houston' city, and has never missed a monthly transaction since becoming a customer.
Example Input:
| customer_id | customer_name | city | opening_balance |
|---|---|---|---|
| 101 | John Doe | Houston | 60000 |
| 102 | Emma Smith | Austin | 40000 |
| 103 | Michael Brown | Houston | 55000 |
| 104 | Jessica Davis | Seattle | 70000 |
Example Input:
| transaction_id | customer_id | transaction_date | missed_transaction |
|---|---|---|---|
| 201 | 101 | 09/08/2022 00:00:00 | no |
| 202 | 102 | 09/08/2022 00:00:00 | yes |
| 203 | 103 | 09/08/2022 00:00:00 | no |
| 204 | 101 | 10/08/2022 00:00:00 | no |
| 205 | 103 | 10/08/2022 00:00:00 | no |
Answer:
This query first joins the and tables on . It then filters for customers whose is more than $50000, and who live in 'Houston'. The clause groups the results by the customer's name and id, and also their transaction completeness to ensure we consider only those customers who have no missed transactions. The clause is used to ensure we only include customers who haven't missed any transactions since they start to become a customer, by comparing the count of transactions for each customer to the total transactions made by the specific customer in the table. This ultimately filters out the customers who meet all the conditions specified.
SQL Question 7: How does and differ?
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 Charles Schwab:
This query retrieves the total salary for each Analytics department at Charles Schwab 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 Charles Schwab departments where the total salary is greater than $1 million
SQL Question 8: Analyzing Click-through Rates for Charles Schwab Investments
Charles Schwab, a prominent brokerage firm, wants to analyze its click-through rates from the investment details page to the invested action initiation page for its digital customers. The company ran multiple campaigns where each campaign has multiple ads, and they are interested in comparing these campaigns based on their click-through rates. They specifically want to know the click-through rate for each campaign during the month of August.
We have two datasets. The first one, , has every campaign's information, including the unique identifier and the .
Sample Input:
| campaign_id | campaign_name |
|---|---|
| 101 | "Retirement savings" |
| 102 | "Education fund" |
| 103 | "Secure your future" |
The second dataset, , includes every click that a user makes. It includes an id , the date when it was made , the campaign that the click belongs to , and whether or not it led to an investment .
Sample Input:
| click_id | click_date | campaign_id | invested |
|---|---|---|---|
| 1001 | "08/01/2022" | 101 | False |
| 1002 | "08/02/2022" | 101 | True |
| 1003 | "08/03/2022" | 102 | False |
| 1004 | "08/04/2022" | 102 | True |
| 1005 | "08/05/2022" | 103 | True |
The goal is to compute the click-through rate per campaign in the month of August. is defined as the number of users who actually invested after clicking (invested = True) divided by the total number of clicks for each campaign.
Answer:
This query first joins and tables on only for the records of August. It then uses a statement inside the function to count only invested clicks, which are then divided by the total number of clicks for each campaign to calculate the click-through rates.
To solve a similar problem on DataLemur's free interactive coding environment, attempt this Facebook SQL Interview question:
SQL Question 9: Maximum Average Transaction Amount per Investment Type
For Charles Schwab, a financial services company, one potential question could be to find the investment type that has the maximum average transaction amount for each client.
Assume there is a table called 'transactions' which has a record of each transaction, including client_id, transaction_date, transaction_amount, and investment_type.
The 'transactions' table has the following schema:
Example Input:
| transaction_id | client_id | transaction_date | investment_type | transaction_amount |
|---|---|---|---|---|
| 101 | 1 | 2020-06-08 | STOCKS | 3600.00 |
| 102 | 2 | 2020-06-10 | BONDS | 5000.00 |
| 103 | 1 | 2020-07-20 | STOCKS | 4200.00 |
| 104 | 3 | 2020-07-26 | BONDS | 8900.00 |
| 105 | 1 | 2020-08-15 | ETF | 5800.00 |
You need to write a SQL query that will fetch the maximum average transaction amount for each client and the corresponding investment type.
Example Output:
| client_id | investment_type | avg_transaction_amount |
|---|---|---|
| 1 | ETF | 5800.00 |
| 2 | BONDS | 5000.00 |
| 3 | BONDS | 8900.00 |
Answer:
This query first groups the transactions based on each client and the type of investment they have made and calculates the average transaction amount for each group. It then includes only those groups where the average transaction amount is the maximum among all the groups for each client. It should return the client_id, the investment type with the maximum average transaction amount, and the corresponding maximum average transaction amount.
Check out the Charles Schwad career page and see what role might be the best fir for you!
SQL Question 10: What are the different types of database indexes?
A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.
There are several types of indexes:
- unique & non-inuqie indexes
- primary & composite indexes
- clustered & non-clustered indexes
Preparing For The Charles Schwab 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 Charles Schwab SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Facebook, Google, and VC-backed startups.
Each problem on DataLemur has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there is an online SQL code editor so you can right in the browser run your SQL query and have it executed.
To prep for the Charles Schwab SQL interview you can also be wise to practice SQL questions from other banking & finanacial services companies like:
In case your SQL query skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as RANK vs. DENSE RANK and CASE/WHEN statements – both of these pop up routinely during SQL interviews at Charles Schwab.
Charles Schwab Data Science Interview Tips
What Do Charles Schwab Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems to practice for the Charles Schwab Data Science Interview are:
- Probability & Stats Questions
- Python Pandas or R Coding Questions
- Product Data Science Interview Questions
- ML Interview Questions
- Behavioral & Resume-Based Questions
How To Prepare for Charles Schwab Data Science Interviews?
I'm a bit biased, but I believe the optimal way to prep for Charles Schwab Data Science interviews is to read the book Ace the Data Science Interview.
The book covers 201 data interview questions taken from Facebook, Google & startups. It also has a refresher covering SQL, Product-Sense & ML. And finally it's vouched for by the data community, which is why it's got over 1000+ 5-star reviews on Amazon.