8 Envestnet SQL Interview Questions (Updated 2024)
At Envestnet, SQL is used across the company for analyzing complex financial data for clients, especially in their data & analytics division. That's why Envestnet asks SQL coding questions during interviews for Data Analyst, Data Science, and BI jobs.
So, to help you ace the Envestnet SQL interview, we've collected 8 Envestnet SQL interview questions – able to answer them all?
8 Envestnet SQL Interview Questions
SQL Question 1: Average Portfolio Balance Per Month
Envestnet is a leading data aggregation and data analytics platform for wealth management market. Assume, for instance, your team is interested in tracking the average balance of all client portfolios per month. You have a table that has total balance for each portfolio at the end of each day.
Here is the schema for the table:
Example Input:
| portfolio_id | date | total_balance |
|---|---|---|
| 1001 | 01/30/2022 | 25000 |
| 1002 | 01/31/2022 | 12000 |
| 1001 | 02/01/2022 | 26000 |
| 1002 | 02/02/2022 | 13000 |
| 1001 | 02/03/2022 | 25500 |
Your task is to write a SQL query that computes the average portfolio balance for each month.
Example Output:
| month | average_balance |
|---|---|
| 1 | 18500 |
| 2 | 21500 |
Answer:
As you can see in the above query, we are using the window function to calculate the average balance for each month. We partition the data by month, so the function calculates an average for each separate month in the column. The function is used to get the month part of the column. Please note the result may vary based on real data.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 2: Average Number of Transactions per Client
At Envestnet, a provider of wealth management technology and services, they may ask you to find the average number of transactions made by each client.
Example Input:
| transaction_id | client_id | transaction_date | transaction_amt |
|---|---|---|---|
| 101 | 77 | 06/01/2022 00:00:00 | 500 |
| 102 | 77 | 06/02/2022 00:00:00 | 700 |
| 103 | 89 | 06/10/2022 00:00:00 | 1200 |
| 104 | 89 | 06/14/2022 00:00:00 | 1500 |
| 105 | 77 | 07/01/2022 00:00:00 | 1000 |
Example Output:
| client_id | avg_transactions |
|---|---|
| 77 | 3 |
| 89 | 2 |
Answer:
In this query, we first select and the count of as from the table and group them by . It gives us the total number of transactions performed by each client. Then, we take the average of for each to get the average number of transactions per client.
To practice a very similar question try this interactive Uber User's Third Transaction Question which is similar for requiring analysis of transactions per user or this Stripe Repeated Payments Question which is similar for focusing on the number and timing of transactions.
SQL Question 3: When would you use denormalization?
Database denormalization is when you add redundancy to a database, and break typical normalization rules (specified by 1st, 2nd, 3rd normal forms). There's a few reasons to denormalize a database:
-
Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases, as joins can be expensive and slow.
-
Simplification: Denormalization can also be used to simplify the design of a database by reducing the number of tables and relationships that need to be managed. This can make it easier to understand and maintain the database.
-
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.
While denormalization can be a useful tool for improving performance and scalability, it's important to keep in mind that it can make update and delete operations more complex. This is because denormalization can create data duplicates, which can make it harder to maintain data integrity. So, to avoid any headaches, it's a good idea to start with a well-normalized database design and then consider denormalization only if it's absolutely necessary for your specific performance and scalability needs. In other words, denormalization can be a bit of a wild card, so it's best to handle it with care!
SQL Question 4: Calculate Click-Through-Conversions
Envestnet, being a company that provides financial and wealth management technology, is interested in understanding the Click-Through-Conversions of its digital products offerings. Envestnet tracks when a user views a product (product_view) and when a product is added to the cart (add_to_cart). Your task is to write a SQL query that calculates the Click-Through-Conversions rates by dividing the number of products added to the cart by the number of product views.
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 1001 | 123 | 07/08/2022 00:00:00 | 50001 |
| 1002 | 356 | 07/10/2022 00:00:00 | 68911 |
| 1003 | 896 | 07/18/2022 00:00:00 | 42591 |
| 1004 | 254 | 07/22/2022 00:00:00 | 50001 |
| 1005 | 190 | 07/05/2022 00:00:00 | 68911 |
Example Input:
| cart_id | user_id | add_date | product_id |
|---|---|---|---|
| 5012 | 356 | 07/10/2022 00:00:00 | 68911 |
| 5013 | 468 | 07/12/2022 00:00:00 | 50001 |
| 5014 | 896 | 07/18/2022 00:00:00 | 42591 |
| 5015 | 123 | 07/24/2022 00:00:00 | 50001 |
| 5016 | 356 | 07/05/2022 00:00:00 | 68911 |
Answer:
What this query does is it first joins the product_view table with the add_to_cart table based on user_id and product_id. Then it groups the result by product_id. For each product_id, it calculates the click-through-conversion rate by counting the distinct number of cart_id (products added to the cart) and dividing it by the distinct number of view_id (products viewed).
To practice a related SQL interview question on DataLemur's free online SQL code editor, attempt this Meta SQL interview question:
SQL Question 5: How do foreign and primary keys differ?
To clarify the distinction between a primary key and a foreign key, let's examine employee data from Envestnet's HR database:
:
+------------+------------+------------+------------+ | employee_id| first_name | last_name | manager_id | +------------+------------+------------+------------+ | 1 | Aubrey | Graham | 3 | | 2 | Marshal | Mathers | 3 | | 3 | Dwayne | Carter | 4 | | 4 | Shawn | Carter | | +------------+------------+------------+------------+
In this table, serves as the primary key. It uniquely identifies each employee and cannot be null.
functions as a foreign key, linking to the of the employee's manager. This establishes a relationship between Envestnet employees and their managers, allowing for easy querying to find an employee's manager or see which employees report to a specific manager.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to connect each employee to their respective department and location.
SQL Question 6: Analyzing Customer and Order Details from Envestnet Database
You are a data analyst at Envestnet and you have been given the task to analyze the customer database and correlate it with their orders. You have two tables: and . The table contains information about all the customers who have ever made a purchase from Envestnet. The table contains detailed information about all the orders that have been made.
The table has the following columns:
- (which is the unique identifier for each customer)
- (the date the customer signed up)
The table has the following columns:
- (which is the unique identifier for each order)
- (the id of the customer who made the order)
Create a SQL query that calculates the total order value per customer for the state of 'California' ('CA') in the year 2021 and orders this output in descending order.
Example Input:
| customer_id | first_name | last_name | signup_date | state |
|---|---|---|---|---|
| 40 | John | Doe | 2019-01-06 | CA |
| 56 | Sarah | Lee | 2020-03-19 | NY |
| 85 | Mark | Davis | 2018-08-09 | CA |
| 99 | Emma | Smith | 2020-12-02 | NJ |
| 102 | Joshua | Brown | 2019-07-20 | CA |
Example Input:
| order_id | order_date | customer_id | product_id | order_value |
|---|---|---|---|---|
| 1507 | 2021-06-12 | 40 | 950 | 70.99 |
| 2593 | 2021-07-25 | 85 | 876 | 140.50 |
| 3461 | 2021-11-11 | 40 | 950 | 70.99 |
| 3920 | 2021-12-22 | 102 | 457 | 111.50 |
| 4265 | 2021-09-03 | 56 | 732 | 86.75 |
Answer:
This query joins the two tables on the field. The function allows us to isolate the year part of the date in the field. The function is used to calculate the total order value per customer. The clause is used to group the results by customer id, and the clause is used to sort the results in descending order of . The clause limits the results to orders from California ('CA') in the year 2021.
Since join questions come up so often during SQL interviews, take a stab at an interactive Spotify JOIN SQL question:
SQL Question 7: When doing database schema design, what's an example of two entities that have a one-to-one relationship? What about one-to-many relationship?
In database schema design, 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 US citizen and their social-security number (SSN) - each citizen has one SSN, and each SSN belongs to one person.
On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. A teacher's relationship with their classes is an example of this - a teacher can teach many classes, but each class is only associated with one teacher.
SQL Question 8: Average Fund Performance by Month
Envestnet is a company that provides unified wealth management technology and services to financial advisors. One major part of their offerings includes financial data aggregation.
Let's say you work for Envestnet and you have a database table containing fund performance data. The data includes fund name, performance date, and rate of return.
Your task is to write a SQL query to calculate the average rate of return for each fund, split by month and year.
Example Input:
| fund_id | fund_name | performance_date | rate_of_return |
|---|---|---|---|
| 101 | 'Fund A' | 01/01/2020 | 0.07 |
| 102 | 'Fund B' | 01/01/2020 | -0.03 |
| 101 | 'Fund A' | 01/02/2020 | 0.06 |
| 102 | 'Fund B' | 01/02/2020 | 0.05 |
| 101 | 'Fund A' | 02/01/2020 | 0.08 |
| 102 | 'Fund B' | 02/01/2020 | -0.04 |
Example Output:
| year | month | fund_name | avg_rate_of_return |
|---|---|---|---|
| 2020 | 1 | 'Fund A' | 0.065 |
| 2020 | 1 | 'Fund B' | 0.01 |
| 2020 | 2 | 'Fund A' | 0.08 |
| 2020 | 2 | 'Fund B' | -0.04 |
Answer:
Using PostgreSQL, the solution query would be:
This query first extracts the year and month from our date field using . Afterwards, it groups the rows by these two values and to calculate the average rate of return. The function is used to perform this average calculation, and the results are then ordered by the year, month, and fund_name for easy read and presentation.
Preparing For The Envestnet 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 Envestnet SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each DataLemur SQL question has multiple hints, full answers and best of all, there's an online SQL code editor so you can right online code up your SQL query and have it executed.
To prep for the Envestnet SQL interview it is also helpful to practice SQL questions from other tech companies like:
But if your SQL skills are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers things like RANK vs. DENSE RANK and filtering strings based on patterns – both of these pop up often during SQL job interviews at Envestnet.
Envestnet Data Science Interview Tips
What Do Envestnet Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems tested in the Envestnet Data Science Interview are:
- Probability & Stats Questions
- Coding Questions in Python or R
- Product-Sense Questions
- ML Modelling Questions
- Behavioral Based Interview Questions
How To Prepare for Envestnet Data Science Interviews?
To prepare for Envestnet Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google)
- a refresher on SQL, AB Testing & ML
- over 900+ reviews on Amazon & 4.5-star rating