At Leidos, a Data Engineer plays a pivotal role in solving some of the world's most complex challenges in defense, intelligence, civil infrastructure, and health markets. You will be responsible for designing, building, and maintaining the robust data pipelines and architectures that power critical decision-making systems. This is not a standard commercial data engineering role; the systems you build directly support mission-critical operations where data integrity, security, and high availability are paramount.

The impact of a Data Engineer at Leidos is felt across massive federal and enterprise systems. Whether you are optimizing data ingestion for defense intelligence platforms or structuring healthcare analytics pipelines, your work ensures that data is secure, accessible, and actionable. The sheer scale of the datasets and the stringent security constraints make this role both technically demanding and deeply rewarding for engineers who want their work to have a tangible, real-world impact.

You will join a highly collaborative environment where software engineers, data scientists, and systems architects work in tandem. Leidos values versatile engineers who can navigate both modern cloud data stacks and legacy on-premises infrastructures. If you are passionate about building resilient data systems that serve the public good and support national security, this position offers an unparalleled platform to grow your career.

The interview questions you will encounter at Leidos are designed to evaluate both your technical depth and your ability to communicate complex concepts. The following questions are representative of what candidates face, compiled from real reported interview experiences. While the exact questions may vary by team and seniority level, they consistently follow key patterns focused on project execution, problem-solving, and personal background.

Technical Projects & Core Computer Science

This category tests your fundamental engineering knowledge and your ability to articulate past technical achievements. Interviewers want to see how you structure your thoughts and explain complex technical decisions.

• Describe a complex technical project related to computer science that you designed and implemented.
• Walk me through the architecture of a data pipeline you built from scratch.

Preparing for an interview at Leidos requires a balanced approach. You must demonstrate strong technical competency while also highlighting your soft skills and adaptability. The hiring team looks for candidates who can seamlessly transition between high-level architectural discussions and hands-on coding.

Role-Related Knowledge – You must show a deep understanding of data warehousing, ETL processes, and database management. Be prepared to discuss the specific tools and frameworks you have used, explaining why they were appropriate for those specific use cases.

Problem-Solving Ability – Interviewers will evaluate how you approach ambiguous technical challenges. They want to see a structured methodology: identifying requirements, discussing trade-offs, and proposing scalable solutions.

Communication & Collaboration – At Leidos, you will interact with diverse teams and stakeholders. Your ability to explain complex technical concepts simply and navigate communication challenges is critical to your success.

The interview process for a Data Engineer at Leidos is structured, transparent, and designed to move efficiently. Candidates typically experience a supportive and conversational environment where the hiring team aims to understand your complete profile as an engineer. The process focuses heavily on practical experience and conversational technical assessments rather than high-stress whiteboard coding.

The journey begins with an initial recruiter screen, followed by a conversation with the division head or hiring manager. This stage is highly conversational, focusing on your background, technical projects, and extracurricular activities. The final stage is a technical interview that dives deeper into your engineering capabilities and problem-solving skills.

This visual timeline illustrates the typical progression from your initial contact to the final decision. Candidates should use this roadmap to pace their preparation, ensuring they are ready for both the high-level project discussions in the early stages and the deeper technical evaluations later on. While the process is streamlined, the technical round requires rigorous preparation.

To succeed in the Leidos interview process, you must understand the specific areas where you will be evaluated. The hiring team assesses candidates across several core competencies to ensure they can handle the unique challenges of the role.

Technical Project Deep-Dives

Your ability to discuss your past work is one of the most heavily weighted parts of the interview. Interviewers will ask you to detail your computer science and data engineering projects to evaluate your practical engineering experience.

Be ready to go over:

• System Architecture – The overall design of your past systems and how different components interacted.
• Technology Selection – The rationale behind choosing specific databases, languages, or frameworks.
• Scaling & Performance – How you designed your systems to handle growth and resolve performance bottlenecks.

Example scenarios:

• "Walk me through a project where you had to integrate multiple disparate data sources into a single repository."
• "Describe a time when a system you built failed in production and how you diagnosed and resolved the issue."

Core Data Engineering & Coding

This area focuses on your fundamental data engineering skills, including database design, query optimization, and pipeline development.

Be ready to go over:

• SQL & Schema Design – Designing efficient schemas and writing optimized queries.
• Data Pipeline Development – Building reliable ETL/ELT pipelines.
• Data Quality – Ensuring data accuracy and consistency across pipelines.
• Advanced concepts – Distributed computing principles (e.g., Spark, Hadoop) and cloud data warehousing architectures.

Example scenarios:

• "How would you design a schema to track real-time asset movements for a logistics system?"
• "Explain the differences between batch and stream processing and when you would use each."