Viasat Inc. Data Engineer Interview Questions + Guide in 2025

Viasat Inc. is a global communications company that connects consumers, businesses, governments, and militaries to help shape the way they communicate.

As a Data Engineer at Viasat, you will play a critical role in designing, building, and automating data processes to support the company's mission of delivering transformative connectivity. Your responsibilities will include collaborating with cross-functional teams to enhance business processes, developing scalable ETL processes, and managing multiple database systems using cloud technologies. A strong proficiency in Python and experience with data architecture, troubleshooting, and performance tuning are essential for this role. Ideal candidates will possess a Bachelor’s degree in a related field and demonstrate the ability to thrive in a fast-paced, agile environment while managing various projects simultaneously.

This guide will equip you with insights and knowledge to prepare effectively for your interview, helping you stand out as a strong candidate for the Data Engineer position at Viasat.

The interview process for a Data Engineer position at Viasat Inc. is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Initial Phone Screen

The process begins with a phone screen, which usually lasts about 30 minutes. During this call, a recruiter will discuss your resume, professional background, and interest in the role. This is also an opportunity for you to learn more about Viasat's mission and values, as well as the specifics of the Data Engineer position.

2. Technical Interview

Following the initial screen, candidates will participate in one or more technical interviews. These interviews focus on your programming skills, particularly in Python, and may include coding challenges that test your ability to solve problems efficiently. You may also be asked to explain your past projects, detailing your role and the technologies you used. Expect questions that assess your understanding of ETL processes, database management, and data pipeline construction.

3. Behavioral Interview

In addition to technical assessments, Viasat places a strong emphasis on cultural fit. The behavioral interview will explore your teamwork abilities, project management skills, and how you handle challenges in a fast-paced environment. Be prepared to discuss specific examples from your past experiences that demonstrate your problem-solving skills and adaptability.

4. Final Interview

The final stage may involve a more in-depth discussion with senior team members or stakeholders. This interview will likely cover both technical and strategic aspects of the role, including your approach to designing scalable data services and your familiarity with cloud technologies. You may also be asked about your experience with various database systems and your ability to collaborate with cross-functional teams.

As you prepare for these interviews, it's essential to be ready for a mix of technical and behavioral questions that will help the interviewers gauge your fit for the role and the company culture. Next, let's delve into the specific interview questions that candidates have encountered during the process.

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

Prepare to Discuss Your Projects

Given that interviewers at Viasat often ask candidates to explain their past projects, be ready to dive deep into your experiences. Highlight the challenges you faced, the technologies you used, and the impact your work had on the project or organization. Make sure to articulate your thought process and decision-making clearly, as this demonstrates your problem-solving skills and technical expertise.

Brush Up on Coding Skills

Expect to encounter coding questions during the interview process. Focus on Python, as it is a key requirement for the role. Practice coding problems that involve data manipulation, ETL processes, and database interactions. Familiarize yourself with common algorithms and data structures, as these may come up in technical discussions.

Understand the Data Engineering Landscape

Viasat is looking for candidates who can design and build scalable data processes. Make sure you understand the principles of ETL, data warehousing, and real-time data processing. Be prepared to discuss your experience with various database technologies, particularly RDBMS like MS SQL, PostgreSQL, or Oracle, and how you have optimized SQL queries for performance.

Familiarize Yourself with Cloud Technologies

As Viasat leverages cloud technologies, having a solid understanding of at least one cloud provider (AWS, GCP, or Azure) will be beneficial. Be ready to discuss how you have used cloud services in your previous roles, particularly in relation to data storage, processing, and management.

Emphasize Collaboration and Teamwork

Viasat values collaboration across cross-functional teams. Be prepared to share examples of how you have worked effectively with others, managed multiple projects, and contributed to a team environment. Highlight your ability to communicate complex technical concepts to non-technical stakeholders, as this is crucial in a collaborative setting.

Showcase Your Adaptability

The fast-paced and dynamic nature of Viasat's work environment means that adaptability is key. Share experiences where you successfully navigated changes or challenges in your projects. This will demonstrate your resilience and ability to thrive in a rapidly evolving landscape.

Align with Company Values

Viasat seeks individuals who think big, act fearlessly, and foster an inclusive environment. Research the company’s mission and values, and be prepared to discuss how your personal values align with theirs. This will not only show your enthusiasm for the role but also your commitment to contributing positively to the company culture.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at Viasat. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Viasat Inc. Candidates should focus on demonstrating their technical expertise, problem-solving abilities, and experience with data engineering projects. Be prepared to discuss your past projects in detail, as well as your approach to building and maintaining data systems.

Technical Skills

1. Can you explain a data pipeline you have built in the past? What technologies did you use?

This question assesses your hands-on experience with data pipelines and the technologies you are familiar with.

How to Answer

Discuss the architecture of the pipeline, the tools you used, and the challenges you faced during implementation. Highlight your role in the project and the impact it had on the organization.

Example

“I built a data pipeline using Apache Airflow to automate the ETL process for our sales data. I utilized Python for scripting and PostgreSQL for the database. One challenge was ensuring data quality, which I addressed by implementing validation checks at each stage of the pipeline.”

2. How do you optimize SQL queries for performance?

This question evaluates your understanding of SQL and your ability to enhance database performance.

How to Answer

Explain the techniques you use to optimize queries, such as indexing, query restructuring, or analyzing execution plans. Provide a specific example if possible.

Example

“I optimize SQL queries by analyzing the execution plan to identify bottlenecks. For instance, I once improved a slow-running report by adding indexes on frequently queried columns, which reduced the execution time from several minutes to under 30 seconds.”

3. Describe your experience with cloud technologies in data engineering.

This question gauges your familiarity with cloud platforms and their application in data engineering.

How to Answer

Discuss the cloud services you have used, how you implemented them in your projects, and the benefits they provided.

Example

“I have experience using AWS for hosting our data warehouse. I utilized S3 for data storage and Redshift for analytics. This setup allowed us to scale our data processing capabilities significantly while reducing costs.”

4. What strategies do you use for data quality assurance in ETL processes?

This question focuses on your approach to maintaining data integrity throughout the ETL process.

How to Answer

Talk about the methods you implement to ensure data quality, such as validation rules, error handling, and monitoring.

Example

“I implement data quality checks at each stage of the ETL process. For example, I use automated scripts to validate incoming data against predefined schemas and set up alerts for any discrepancies, ensuring that only clean data enters our systems.”

5. Can you discuss a challenging data engineering problem you faced and how you resolved it?

This question allows you to showcase your problem-solving skills and resilience in the face of challenges.

How to Answer

Describe the problem, your thought process in addressing it, and the outcome of your solution.

Example

“I faced a challenge when our data ingestion process was failing due to unexpected data formats. I quickly implemented a flexible schema that could handle variations and added logging to capture errors. This not only resolved the immediate issue but also improved our data processing reliability.”

Project Experience

6. Tell me about a project where you collaborated with cross-functional teams.

This question assesses your teamwork and communication skills in a data engineering context.

How to Answer

Highlight your role in the project, the teams involved, and how you facilitated collaboration.

Example

“I worked on a project to integrate marketing data with our sales database. I collaborated with the marketing team to understand their data needs and with the IT team to ensure our infrastructure could support the integration. Regular meetings helped us stay aligned and address any issues promptly.”

7. How do you stay updated with the latest trends and technologies in data engineering?

This question evaluates your commitment to continuous learning and professional development.

How to Answer

Discuss the resources you use to keep your skills sharp, such as online courses, webinars, or industry publications.

Example

“I regularly follow data engineering blogs and participate in online forums. I also take courses on platforms like Coursera to learn about new tools and technologies. Recently, I completed a course on Apache Spark, which I found very beneficial.”

8. Describe a time when you had to troubleshoot a data issue. What steps did you take?

This question tests your analytical skills and your approach to problem-solving.

How to Answer

Outline the steps you took to identify and resolve the issue, emphasizing your analytical thinking.

Example

“When we noticed discrepancies in our reporting data, I first checked the ETL logs to identify where the data was failing. I traced the issue back to a transformation step that was incorrectly configured. After correcting the configuration, I implemented additional logging to prevent similar issues in the future.”

9. What tools do you prefer for version control, and why?

This question assesses your familiarity with version control systems and their importance in data engineering.

How to Answer

Discuss the tools you have used and the advantages they offer in managing code and collaboration.

Example

“I prefer using Git for version control because it allows for easy collaboration and branching. It’s essential for managing changes in our ETL scripts and ensuring that we can roll back to previous versions if needed.”

10. How do you approach learning a new technology or tool?

This question evaluates your adaptability and willingness to learn.

How to Answer

Describe your learning process and how you apply new knowledge to your work.

Example

“When learning a new technology, I start with online tutorials to understand the basics. I then apply what I’ve learned in a small project to reinforce my understanding. For instance, when I started using Docker, I created a containerized version of our ETL process to see how it could improve our deployment workflow.”