At GE Aerospace, a Data Engineer plays a critical role in powering the future of flight. The company operates on a massive global scale, designing and manufacturing world-class jet engines, components, and integrated systems for commercial and military aircraft. In this role, you are responsible for building, optimizing, and maintaining the highly sophisticated data pipelines that ingest and process petabytes of telemetry, manufacturing, and IoT sensor data from aircraft engines around the world.

Your work directly impacts flight safety, predictive maintenance, and operational efficiency. By transforming raw, high-frequency sensor streams into clean, structured, and actionable datasets, you enable data scientists and aerospace engineers to build models that predict engine wear, optimize fuel consumption, and prevent unscheduled maintenance events. This is not just a standard enterprise data pipeline; it is a mission-critical operation where data integrity and processing speed have real-world safety implications.

To succeed as a Data Engineer here, you must be comfortable working with a massive and highly diverse technology stack. GE Aerospace utilizes a wide array of big data tools, distributed computing frameworks, and cloud platforms. You will be expected to demonstrate deep technical versatility, managing everything from low-level automation scripts to massive parallel processing jobs, all while ensuring absolute reliability and performance.

The questions you will face during the GE Aerospace interview process are designed to evaluate both your practical engineering skills and your ability to navigate complex, real-world data scenarios. These questions are drawn from real interview experiences and are categorized below to help you structure your preparation. Rather than memorizing specific answers, focus on understanding the underlying engineering principles and how to communicate your technical decisions clearly.

Big Data & Spark Optimization

These questions assess your understanding of distributed computing systems and your ability to write efficient, scalable code to process massive datasets.

• How do you diagnose and resolve data skew in a Spark application?
• Explain the difference between client mode and cluster mode in Spark on YARN.

Preparing for an interview at GE Aerospace requires a balanced approach. You must demonstrate deep technical expertise in distributed systems and database management, while also showing that you can communicate effectively and collaborate across complex organizational structures.

Role-related knowledge – You must have a strong command of the core technologies in the GE Aerospace stack, particularly Spark, SQL, and Bash scripting. Be prepared to explain not just how to use these tools, but the underlying mechanics of how they operate, manage memory, and scale.

Problem-solving ability – Interviewers will present you with open-ended, scenario-based engineering challenges. They want to see how you structure your thoughts, ask clarifying questions, identify constraints, and systematically design a scalable solution.

Leadership & the STAR method – For behavioral questions, you must structure your answers using the STAR method (Situation, Task, Action, Result). Focus heavily on your personal contribution (what you did) and the quantifiable impact of your actions on the business or team.

Adaptability – Operating in a large enterprise means dealing with shifting priorities, legacy systems, and diverse stakeholders. You should demonstrate a pragmatic mindset, showing that you can deliver value within existing constraints while advocating for modern engineering practices.

The interview process for a Data Engineer at GE Aerospace is designed to evaluate your technical depth, problem-solving capabilities, and cultural fit. While the exact steps can vary slightly depending on the specific team, location, and seniority of the role, the process generally moves from initial screening to deep technical evaluation and behavioral assessment.

The journey typically begins with a standard Human Resources screening to align on your background, expectations, and interest in the role. Following this, you will face a rigorous technical evaluation. For many teams, this includes an online technical test or a dedicated technical interview focused heavily on big data frameworks, database management, and scripting. The final stages involve deeper scenario-based discussions and behavioral interviews with multiple team members and engineering leaders.

The timeline above outlines the typical progression of the hiring process. Candidates should expect the technical evaluation phase to be the most demanding, requiring focused preparation on both coding and system design concepts. Use this timeline to pace your preparation, ensuring you are fully prepared for the technical test before moving into the scenario-based rounds.

To pass the technical bar at GE Aerospace, you must demonstrate mastery across several core competencies. Interviewers will drill down into these specific areas to assess your readiness for the complex data challenges you will face on the job.

Distributed Computing & Spark Optimization

This is a cornerstone of the technical evaluation. Because GE Aerospace processes massive volumes of aviation and IoT data, you must know how to write highly efficient distributed processing jobs.

Be ready to go over:

• Spark Memory Management – How driver and executor memory are allocated, and how to tune them to prevent OutOfMemory (OOM) errors.
• Data Skew Mitigation – Techniques such as salting, broadcast joins, and repartitioning to handle unevenly distributed data.
• Execution Plans – How to read and interpret a Spark physical plan to identify bottlenecks, unnecessary shuffles, or serialization issues.
• Advanced concepts (less common) – Custom partitioners, dynamic resource allocation tuning, and writing optimized user-defined functions (UDFs) in Scala or Python.

Example scenarios:

• "You have a Spark job that joins a massive 10 TB dataset with a small 50 MB lookup table, and it is running extremely slowly. How do you optimize this join?"
• "A daily aggregation job is failing with an executor lost error. Walk me through how you would debug and fix this issue."

Database Management & Pipeline Automation

Data engineers are responsible for the entire lifecycle of data, from ingestion to consumption. You must show that you can build reliable, automated pipelines and write high-performance queries.

Be ready to go over:

• SQL Proficiency – Writing complex queries, using window functions, CTEs, and optimizing execution paths.
• Bash Scripting – Automating file transfers, parsing logs, handling environment variables, and scheduling basic cron jobs.
• Data Modeling – Designing schemas (star, snowflake, or time-series) that balance write performance with query latency.

Example scenarios:

• "Write a Bash script that monitors a directory for incoming sensor log files, checks if they are valid, and loads them into a database, logging any errors to a separate file."
• "Design a schema to support a dashboard that displays real-time engine performance metrics alongside historical baseline data."

Scenario-Based Engineering & Architecture

Interviewers want to see how you apply your technical skills to real-world, ambiguous scenarios. They will evaluate your architectural instincts and your ability to make pragmatic engineering decisions.

Be ready to go over:

• Pipeline Reliability – Implementing retry mechanisms, data validation checks, and alerting systems.
• Data Lake Architecture – Structuring raw, conformed, and curated layers in a cloud-based data lake environment.
• Batch vs. Streaming – Choosing the right processing paradigm based on latency requirements and resource costs.

Example scenarios:

• "A critical data source has changed its schema without warning, causing downstream pipelines to fail. How do you design your pipeline to handle this gracefully without losing data?"
• "How would you architect a pipeline to ingest and process sensor data from 5,000 aircraft engines landing daily, ensuring the data is available for analysis within one hour of landing?"

Behavioral & STAR Methodology

Technical skills alone are not enough. You must demonstrate that you can work effectively within a highly collaborative, safety-conscious engineering organization.

Be ready to go over:

• Collaboration – Working with cross-functional teams, including data scientists, product managers, and systems engineers.
• Ownership – Taking responsibility for a system or pipeline, identifying areas for improvement, and driving those changes to completion.
• Conflict Resolution – Navigating technical disagreements or managing competing priorities in a fast-paced environment.

Example scenarios:

• "Describe a time when you were tasked with migrating a legacy data system to a modern stack. What challenges did you face, and how did you manage the transition?"
• "Tell me about a time you made a mistake that impacted a production system. How did you handle it, and what did you learn?"