To succeed at Viasat, you must demonstrate that you are more than a coder; you are a problem solver who understands the lifecycle of data. Your preparation should focus on articulating the impact of your technical choices and demonstrating a methodical approach to engineering challenges.

Technical Proficiency – You must be comfortable discussing your past projects in granular detail, including the architecture, the tools used, and the specific problems you solved. Be ready to defend your choice of technologies and explain how you optimized for performance or scalability.

Problem-Solving Methodology – Interviewers are looking for a logical, structured approach to ambiguity. When presented with a case study or a hypothetical design question, start by defining the requirements, identifying potential bottlenecks, and proposing a solution that balances speed, cost, and reliability.

Collaboration and Communication – As a Data Engineer, you will work closely with data scientists, product managers, and software engineers. Be prepared to explain how you communicate technical constraints to non-technical stakeholders and how you contribute to team-based development environments.

The Viasat interview process is designed to be rigorous but fair, focusing on your past experience and your ability to apply technical concepts to real-world scenarios. It typically begins with a recruiter or hiring manager screen to gauge your interest and background, followed by a series of technical interviews. You should expect a mix of coding assessments and deep-dive technical discussions centered on your resume.

The visual timeline outlines the typical progression from the initial contact to the final decision. Candidates should view this as a structured journey: the phone screen establishes your baseline, while the subsequent rounds progressively increase in technical complexity. Pace your preparation so that you are refreshed for the technical deep-dive rounds, which often require significant cognitive energy.

Data Architecture and Pipeline Design

This area evaluates your ability to design systems that are both scalable and maintainable. You will be expected to discuss how data flows from source to destination and how you ensure reliability.

Be ready to go over:

• ETL/ELT processes – Defining the workflow for moving and transforming data.
• Data modeling – Choosing the right schema for analytical needs.
• Error handling – Strategies for managing data quality issues in real-time.

Example questions or scenarios:

• "How would you design a pipeline to handle a sudden spike in data volume?"
• "Describe a time you had to refactor a legacy pipeline to improve performance."

Coding and Scripting

Your ability to write clean, efficient, and well-documented code is non-negotiable. While you do not need to be a competitive programmer, you must demonstrate strong proficiency in languages like Python or SQL.

Be ready to go over:

• Data structures – Knowing when to use lists, dictionaries, or sets for efficient lookups.
• SQL optimization – Understanding indexing, partitioning, and query execution plans.
• Code reviews – How you handle constructive feedback on your code.

Example questions or scenarios:

• "Write a script to process a large CSV file and aggregate values by a specific key."
• "What are the common pitfalls when working with distributed data processing frameworks?"