Sunrun Data Engineer Interview Questions + Guide in 2025

Sunrun is a leading provider of residential solar electricity, committed to transforming the way we power our lives through innovative energy solutions.

As a Data Engineer at Sunrun, you will play a crucial role in managing and optimizing the performance of our solar fleet through advanced telemetry data analysis, process automation, and the development of comprehensive monitoring platforms. Key responsibilities include building and maintaining complex analytical projects, integrating third-party data sources, and collaborating with various teams to enhance operational efficiency. You will be responsible for delivering critical code, making core technical decisions, and ensuring the integrity of data from ingestion to automation output.

The role requires a strong background in data science or software development, with proficiency in SQL and Python, as well as experience in developing algorithms and predictive models. Ideal candidates will possess excellent analytical skills, a results-oriented mindset, and a capacity for storytelling through data insights. At Sunrun, we highly value diversity, inclusion, and teamwork, so being a self-starter who thrives in a collaborative environment is essential.

This guide will equip you with insights into the specific skills and experiences that Sunrun values in a Data Engineer, helping you to effectively articulate your fit for the role during the interview process.

The interview process for a Data Engineer at Sunrun is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages designed to evaluate your experience, problem-solving abilities, and alignment with Sunrun's values.

1. Initial Phone Screen

The process begins with an initial phone screen, usually conducted by a recruiter. This conversation lasts about 30 minutes and focuses on your background, relevant skills, and motivations for applying to Sunrun. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring you have a clear understanding of what to expect.

2. Technical Interview

Following the initial screen, candidates typically participate in a technical interview. This may be conducted via video call and will focus on your proficiency in SQL and Python, as well as your understanding of algorithms and data structures. Expect to solve coding problems and discuss your previous projects, particularly those that demonstrate your ability to handle large datasets and complex analytical tasks.

3. Panel Interview

The next step is often a panel interview, which includes multiple team members from different departments. This round assesses your collaborative skills and how well you can communicate technical concepts to both technical and non-technical stakeholders. You may be asked to present a past project or case study, highlighting your analytical approach and the impact of your work.

4. Behavioral Interview

In addition to technical skills, Sunrun places a strong emphasis on cultural fit. The behavioral interview will explore your past experiences, work ethic, and how you handle challenges. Questions may focus on teamwork, problem-solving, and your ability to adapt in a fast-paced environment. This is an opportunity to showcase your alignment with Sunrun's commitment to diversity, inclusion, and innovation.

5. Final Interview

The final interview is typically with a senior leader or hiring manager. This round may include a mix of technical and behavioral questions, as well as discussions about your long-term career goals and how they align with Sunrun's mission. You may also be asked to complete a take-home assignment or case study that demonstrates your analytical skills and ability to derive insights from data.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and past experiences.

Here are some tips to help you excel in your interview.

Understand the Company Culture

Sunrun emphasizes diversity, inclusion, and belonging, which are core values that shape their work environment. Familiarize yourself with their mission to transform energy consumption and how your role as a Data Engineer contributes to that vision. Be prepared to discuss how your personal values align with the company’s commitment to sustainability and innovation.

Prepare for Technical Proficiency

Given the importance of SQL and algorithms in this role, ensure you are well-versed in these areas. Brush up on your SQL skills, focusing on complex queries, data manipulation, and performance optimization. Additionally, be ready to discuss algorithms and their applications in data processing and analysis. Consider practicing coding challenges that involve data structures and algorithms to demonstrate your problem-solving abilities.

Showcase Your Analytical Skills

As a Data Engineer, you will be expected to analyze both structured and unstructured data. Prepare to discuss your experience with data cleaning, outlier detection, and statistical analysis. Be ready to provide examples of how you have used data to drive business decisions or improve processes in previous roles. Highlight your ability to synthesize complex data into actionable insights.

Communicate Clearly and Effectively

You will need to present your analyses to both technical and non-technical stakeholders. Practice explaining complex technical concepts in simple terms. Use storytelling techniques to convey the significance of your findings and how they impact business objectives. This will demonstrate your ability to bridge the gap between technical and business teams.

Be Ready for Behavioral Questions

Expect questions that assess your teamwork, adaptability, and problem-solving skills. Reflect on past experiences where you faced challenges or conflicts and how you resolved them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your contributions and the outcomes of your actions.

Engage with Your Interviewers

During the interview, foster a collaborative atmosphere by asking insightful questions about the team dynamics, ongoing projects, and the company’s future direction. This not only shows your interest in the role but also helps you gauge if the company is the right fit for you. Be genuine and personable; your ability to connect with the interviewers can leave a lasting impression.

Prepare for a Panel Interview

If your interview involves multiple team members, be prepared to engage with each interviewer. Understand that they may have different perspectives and priorities. Tailor your responses to address their specific interests, whether they are technical, operational, or strategic. This will demonstrate your versatility and ability to work across departments.

Follow Up Thoughtfully

After the interview, send a personalized thank-you note to express your appreciation for the opportunity to interview. Reiterate your enthusiasm for the role and briefly mention a key point from the discussion that resonated with you. This not only shows professionalism but also reinforces your interest in the position.

By following these tips, you can position yourself as a strong candidate for the Data Engineer role at Sunrun. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Sunrun. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data management and analysis. Be prepared to discuss your past projects, your approach to data engineering challenges, and how you can contribute to Sunrun's mission of transforming energy solutions.

Technical Skills

1. What is your experience with SQL, and can you provide an example of a complex query you have written?

Understanding SQL is crucial for a Data Engineer role, as it is often used for data manipulation and retrieval.

How to Answer

Discuss your familiarity with SQL, emphasizing any complex queries you've constructed, such as those involving multiple joins or subqueries.

Example

"I have extensive experience with SQL, particularly in writing complex queries for data extraction and analysis. For instance, I once wrote a query that joined multiple tables to aggregate sales data by region, filtering out any records with missing values to ensure data integrity."

2. Can you explain the difference between relational databases and NoSQL databases?

This question assesses your understanding of different data storage solutions.

How to Answer

Highlight the key differences, such as structure, scalability, and use cases for each type of database.

Example

"Relational databases use structured query language (SQL) and are designed for structured data, while NoSQL databases are more flexible and can handle unstructured data. For example, I prefer NoSQL for projects requiring high scalability and rapid data retrieval, such as real-time analytics."

3. Describe a time when you had to clean a messy dataset. What approach did you take?

Data cleaning is a critical part of a Data Engineer's responsibilities.

How to Answer

Detail the specific steps you took to clean the data, including identifying outliers, handling missing values, and ensuring data consistency.

Example

"In a previous project, I encountered a dataset with numerous missing fields and outliers. I first used statistical methods to identify outliers and then applied imputation techniques for missing values. This process improved the dataset's quality significantly, leading to more accurate analysis results."

4. How do you ensure the quality and integrity of data in your projects?

This question evaluates your approach to data governance.

How to Answer

Discuss the methods you use to validate and verify data, such as automated testing, data profiling, and regular audits.

Example

"I implement data validation checks at various stages of the data pipeline, including automated tests to catch anomalies early. Additionally, I conduct regular audits to ensure data integrity and compliance with our quality standards."

5. What experience do you have with data warehousing solutions?

Understanding data warehousing is essential for managing large datasets.

How to Answer

Share your experience with specific data warehousing technologies and how you've utilized them in past projects.

Example

"I have worked with Amazon Redshift and Google BigQuery for data warehousing. In one project, I designed a data warehouse schema that optimized query performance, allowing our analytics team to generate reports much faster."

Machine Learning and Analytics

1. Can you explain the concept of feature engineering and its importance?

Feature engineering is a key aspect of preparing data for machine learning models.

How to Answer

Define feature engineering and discuss its role in improving model performance.

Example

"Feature engineering involves creating new input features from existing data to improve model accuracy. For instance, I once derived a 'customer lifetime value' feature from transaction data, which significantly enhanced our predictive models for customer retention."

2. Describe a machine learning project you have worked on. What was your role?

This question assesses your practical experience with machine learning.

How to Answer

Outline your contributions to the project, including data preparation, model selection, and evaluation.

Example

"I worked on a project to predict energy consumption using historical data. My role involved cleaning the dataset, selecting appropriate features, and training various models. I ultimately chose a random forest model, which provided the best accuracy for our predictions."

3. How do you handle overfitting in machine learning models?

Understanding overfitting is crucial for building robust models.

How to Answer

Discuss techniques you use to prevent overfitting, such as cross-validation and regularization.

Example

"I use cross-validation to assess model performance on unseen data and apply regularization techniques like Lasso or Ridge regression to reduce overfitting. This approach helps ensure that the model generalizes well to new data."

4. What metrics do you use to evaluate the performance of a machine learning model?

This question tests your knowledge of model evaluation.

How to Answer

Mention specific metrics relevant to the type of model you are discussing, such as accuracy, precision, recall, or F1 score.

Example

"I typically use accuracy and F1 score for classification models, as they provide a good balance between precision and recall. For regression models, I prefer metrics like RMSE and R-squared to evaluate performance."

5. How do you approach the integration of third-party data sources into your projects?

This question assesses your experience with data integration.

How to Answer

Discuss your process for evaluating, acquiring, and integrating external data sources.

Example

"I start by assessing the quality and relevance of the third-party data. Once I confirm its suitability, I use APIs or ETL processes to integrate it into our systems, ensuring that the data is properly formatted and aligned with our existing datasets."