11 Enphase Energy SQL Interview Questions (Updated 2024)

Updated on

March 1, 2024

At Enphase Energy, SQL is used day-to-day for analyzing power output data from solar panels and organizing customer usage databases for more accurate energy forecasting. Because of this, Enphase Energy frequently asks SQL questions in interviews for Data Science and Data Engineering positions.

As such, to help you prepare for the Enphase Energy SQL interview, this blog covers 11 Enphase Energy SQL interview questions – able to answer them all?

11 Enphase Energy SQL Interview Questions

SQL Question 1: Analyzing Energy Production Data

Enphase Energy deals with solar energy production and distribution. A common need in such a business may be to analyze energy production over time for different solar arrays. For instance, suppose your task is to write a PostgreSQL SQL query to calculate the monthly total, the monthly mean solar energy produced for each solar array, and the running total of the solar energy produced by each array.

Given an table containing daily records of solar energy produced.

Example Input:

Our job would be to provide a monthly summary of the energy produced by each array, as well as a running total. The output table could look like the following:

Example Output:


The window function calculates the running total of energy produced by each solar array. The clause gets the running total by array, and the and clauses make sure that the running total is calculated in the correct order of time (from the start to the current row).

For more window function practice, try this Uber SQL problem on DataLemur's online SQL code editor:

Uber Window Function SQL Interview Question

SQL Question 2: Energy Production by Solar Panel for Enphase Energy

Enphase Energy is a company that designs and manufactures software-driven home energy solutions that span solar generation, home energy storage, web-based monitoring and control, and home automation. Part of their work involves tracking the amount of energy produced by each solar panel installed in a home.

As an SQL Developer, you were tasked with designing a database and writing a query that can help track the daily energy production of individual solar panels installed in multiple homes.

The database has three tables:

  • stores information about each solar panel
  • stores information about each home where the solar panels are installed
  • stores the daily energy production for each solar panel

Here's the sample data:

201"123 Main St""John Doe"
202"456 Oak Ave""Jane Smith"
203"789 Pine Ln""Sam Johnson"

Your task is to write a query that will return the total energy produced by each home for a given date. The result should be ordered by the total energy produced in descending order.


This query joins the three tables based on their relationships, uses the clause to filter the data for a specific date, used clause to group data by home and then calculates the total energy produced by each home by summing up the daily energy produced from all panels installed in the home. The clause then orders the result by the total energy produced in descending order.

SQL Question 3: What are database views, and when would you use them?

A database view is a virtual table that is created based on the results of a SELECT statement, and provides you a customized, read-only version of your data that you can query just like a regular table.

Views in SQL can help you enforce data security requirements by hiding sensitive data from certain users, and can improve performance for some queries by pre-computing the results for an intermediate step and storing them in a view (which can be faster than executing the intermediate query each time). However, their read-only nature means that on any underlying update, the view has to be re-computed.

Enphase Energy SQL Interview Questions

SQL Question 4: Calculate the Average Energy Generation Output of a Solar Panel

Enphase Energy is a company that works in the solar energy sector. They are keen on monitoring the efficiency of their solar panels. Here's a hypothetical situation:

As a data analyst at Enphase Energy, you've been tasked to track the performance of solar panels over a month-long period. More specifically, the stakeholders want to know the average energy output (in kWh) from each solar panel model per day.

Enphase Energy has two main tables:

Example Input:
1Model X
2Model Y
3Model X
4Model Z
Example Input:

The data above shows that there are three models of panels (Model X, Y, and Z). The total power generated by each panel is logged daily.


This SQL script first joins the and tables on . It then groups by and calculates the average , which gives the daily energy output for each solar panel model.

To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for tracking and calculating usage over time or this Twitter Tweets' Rolling Averages Question which is similar for averaging data entries over a period.

SQL Question 5: What is the purpose of the SQL constraint ?


The UNIQUE constraint is used to ensure the uniqueness of the data in a column or set of columns in a table. It prevents the insertion of duplicate values in the specified column or columns and helps to ensure the integrity and reliability of the data in the database.

For example, say you were on the Marketing Analytics team at Enphase Energy and were doing some automated keyword research:

Your keyword database might store SEO data like this:

In this example, the UNIQUE constraint is applied to the "keyword" field to ensure that each keyword is unique. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two rows had the same keyword.

SQL Question 6: Analyzing Click-Through Rate for Enphase Energy Ads

Given a table tracking various characteristics of customer interactions with ads for different Enphase Energy products, calculate the click-through rate (CTR) for each product. The CTR is defined as the number of clicks divided by the number of views.

Example Input:


Example Output:

This PostgreSQL query calculates the total number of clicks and views for each product by using the function in the statement. To calculate the click-through rate, it divides the total clicks by the total views, casting the clicks to decimal to avoid integer division. The results are grouped by and ordered by in descending order so the product with the highest click-through rate will appear first.

To practice a related SQL problem on DataLemur's free online SQL code editor, solve this Meta SQL interview question: SQL interview question asked by Facebook

SQL Question 7: When would you use a clustered index vs. a non-clustered index?

A clustered index is an index where the order of the rows in the database corresponds to the order of the rows in the index. Because of this, a table can only have one clustered index, but it can have multiple non-clustered indexes.

The main difference between the two is that the database tries to keep the data in the same order as the corresponding keys in the clustered index. This can improve the performance of most query operations, as it provides a linear-access path to the data stored in the database.

SQL Question 8: Find Customers Ordered Solar Panels.

In Enphase Energy, a solar energy-based company, a SQL database table named is being maintained which consists of records of all solar panel orders done by various customers. The columns include , , , and . Your task is to write a SQL query to find all the orders placed by customers that have 'Solar Panel' in the product name.

Example Input:
101100106/08/2022 00:00:00Solar Panel A10
102100206/10/2022 00:00:00Solar Panel B20
103100306/10/2022 00:00:00Energy Storage25
104100407/26/2022 00:00:00Solar Panel C15
105100507/05/2022 00:00:00Solar inverter B6
Example Output:
101100106/08/2022 00:00:00Solar Panel A10
102100206/10/2022 00:00:00Solar Panel B20
104100407/26/2022 00:00:00Solar Panel C15


This SQL query uses the LIKE keyword to filter out all the orders with 'Solar Panel' in the product name. The symbol is a wildcard character that matches any sequence of characters. By putting after 'Solar Panel', we are asking SQL to find any records where the product_name starts with 'Solar Panel' followed by any characters.

SQL Question 9: Analyze and Join Customer and Order Tables


As a data analyst at Enphase Energy, you have been asked to extract useful insights from the company's customers and their orders. Specifically, your task is to write a SQL query that will return the total amount spent by each customer in ascending order of their total spend amount. This requires joining your and tables.

Sample Input:




In this SQL query, we join the table with the table on the field using a LEFT JOIN operation. This allows us to maintain all the records of the table, regardless if they have any matching records in the table. We then group the joined table by the , , and of the customers. The operation is used to get the total amount spent by each customer. Finally, we order the resulting table by the column to fulfill the request in the question.

Since joins come up frequently during SQL interviews, try this interactive Snapchat Join SQL question: Snapchat Join SQL question

SQL Question 10: Why would it make sense to denormalize a database?

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 Enphase Energy. 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 11: Calculating Energy Efficiency

To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for calculating ratios rounded to 2 decimal places or this Alibaba Compressed Mean Question which is similar for calculating averages over a period.

Enphase Energy SQL Interview Tips

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Enphase Energy SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the earlier Enphase Energy SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies. DataLemur Question Bank

Each exercise has hints to guide you, detailed solutions and best of all, there is an online SQL coding environment so you can right online code up your SQL query and have it graded.

To prep for the Enphase Energy SQL interview it is also wise to solve SQL questions from other tech companies like:

In case your SQL query skills are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.

Interactive SQL tutorial

This tutorial covers things like INTERCEPT/EXCEPT and joining a table to itself – both of these come up frequently during Enphase Energy interviews.

Enphase Energy Data Science Interview Tips

What Do Enphase Energy Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems covered in the Enphase Energy Data Science Interview are:

Enphase Energy Data Scientist

How To Prepare for Enphase Energy Data Science Interviews?

The best way to prepare for Enphase Energy Data Science interviews is by reading Ace the Data Science Interview. The book's got:

  • 201 Interview Questions from FAANG & startups
  • A Refresher covering SQL, Product-Sense & ML
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Ace the Data Science Interview by Nick Singh Kevin Huo