Supernal Data Engineer Interview Questions + Guide in 2025

Supernal is an Advanced Air Mobility (AAM) company focused on developing electric vertical take-off and landing (eVTOL) vehicles and the necessary ground-to-air ecosystem to support this innovative industry.

As a Data Engineer at Supernal, you will play a pivotal role in the development of tools and solutions that manage the extensive data generated for testing, evaluating, and certifying next-generation aircraft. Key responsibilities include designing and building data management solutions that integrate diverse aircraft test data, ensuring its accessibility and usability across various departments such as IT, Engineering, and Manufacturing. You will also be tasked with constructing data pipelines to facilitate the transfer of information from multiple sources and developing applications for data visualization and analytics.

To excel in this role, candidates should possess a strong foundation in data storage solutions, particularly within cloud environments like AWS or Azure, and have significant experience in developing and maintaining high-performance APIs. Proficiency in both structured and unstructured data management, as well as scripting languages such as Python, is essential. Familiarity with vehicle communication protocols and a background in the aircraft or passenger vehicle R&D industry will further enhance your fit for this position. Supernal values collaboration, innovative thinking, and a proactive approach to problem-solving, making these traits crucial for success.

This guide aims to equip you with the knowledge and insights needed to prepare effectively for your interview, allowing you to showcase your qualifications and alignment with Supernal's mission and values.

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

1. Initial Screening

The first step in the interview process is a phone call with a hiring manager. This conversation usually lasts around 30 minutes and serves as an opportunity for the hiring manager to gauge your background, experience, and motivation for applying to Supernal. You may also discuss the role's expectations and the company's vision, allowing you to determine if it aligns with your career goals.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview. This may involve a video call where you will be asked to demonstrate your proficiency in data engineering concepts, including data storage solutions, API development, and database management. Expect to discuss your experience with cloud environments like AWS or Azure, as well as your familiarity with scripting languages such as Python. You may also be asked to solve practical problems or case studies relevant to the role.

3. Loop Interview

The next stage is a loop interview, which consists of multiple one-on-one interviews with various team members. This round is designed to evaluate your technical skills further, as well as your ability to collaborate with cross-functional teams. You may encounter questions related to data pipeline construction, data visualization, and your experience in the aircraft or automotive R&D industry. Be prepared to discuss your past projects and how they relate to the responsibilities of the Data Engineer role.

4. Final Wrap-Up

The final step in the interview process may include a wrap-up call with a recruiter. This call is intended to address any remaining questions you might have about the role or the company. However, it’s worth noting that some candidates have reported instances where this step did not occur, so it’s advisable to be proactive in seeking feedback or clarification if needed.

As you prepare for your interviews, consider the specific skills and experiences that will be relevant to the questions you may face. Next, we will delve into the types of questions that candidates have encountered during the interview process.

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

Prepare for a Structured Interview Process

The interview process at Supernal typically involves a call with a hiring manager followed by a loop interview with multiple interviewers. Given the feedback from previous candidates, it's essential to be prepared for potential delays or changes in the schedule. Make sure to confirm the interview details a day prior and have a backup plan in case of technical issues. Being adaptable and patient will reflect positively on your professionalism.

Showcase Your Technical Expertise

As a Data Engineer, your proficiency in data storage solutions, cloud environments (AWS, Azure), and API development will be crucial. Be ready to discuss specific projects where you utilized these skills. Prepare to explain your approach to building data pipelines and managing both structured and unstructured data. Highlight any experience you have with vehicle communication protocols and data visualization tools, as these are particularly relevant to the role.

Emphasize Collaboration and Communication Skills

Supernal values collaboration across various teams, including IT, Engineering, and Manufacturing. Be prepared to discuss how you have successfully worked in cross-functional teams in the past. Highlight your communication skills, especially in conveying complex technical concepts to non-technical stakeholders. This will demonstrate your ability to contribute to the collaborative culture at Supernal.

Understand the Company Culture

Supernal promotes a creative and collaborative work environment. Familiarize yourself with their core values and be ready to discuss how your personal values align with the company’s mission. Show enthusiasm for the innovative work they are doing in the Advanced Air Mobility sector and express your eagerness to contribute to their vision of sustainable and human-centered mobility solutions.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, adaptability, and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. For example, you might discuss a time when you had to troubleshoot a data pipeline issue or how you managed competing priorities in a project.

Follow Up with Thoughtful Questions

At the end of the interview, you will likely have the opportunity to ask questions. Prepare thoughtful inquiries that reflect your interest in the role and the company. You might ask about the team dynamics, the tools and technologies they use, or how they measure success in the Data Engineering team. This not only shows your engagement but also helps you assess if Supernal is the right fit for you.

By following these tips, you can present yourself as a well-prepared and enthusiastic candidate who is ready to contribute to Supernal's mission. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Supernal. The interview process will likely focus on your technical skills, experience with data management, and ability to work collaboratively in a fast-paced environment. Be prepared to discuss your background in data storage solutions, API development, and your familiarity with cloud environments.

Technical Skills

1. Can you explain your experience with cloud data storage solutions, particularly in AWS or Azure?

This question assesses your familiarity with cloud environments, which is crucial for the role.

How to Answer

Discuss specific projects where you utilized cloud storage solutions, emphasizing your role and the technologies you used.

Example

“I have over five years of experience working with AWS, where I designed and implemented data storage solutions for a large-scale application. I utilized S3 for object storage and RDS for relational databases, ensuring data accessibility and security.”

2. Describe a data pipeline you have built. What challenges did you face, and how did you overcome them?

This question evaluates your practical experience in building data pipelines.

How to Answer

Detail the architecture of the pipeline, the tools you used, and the specific challenges you encountered, along with your solutions.

Example

“I built a data pipeline using Apache Kafka and AWS Lambda to process real-time data from IoT devices. One challenge was ensuring data integrity during transmission, which I addressed by implementing a robust error-handling mechanism that retried failed transmissions.”

3. How do you ensure data quality and integrity in your projects?

This question focuses on your approach to maintaining high data standards.

How to Answer

Discuss the methods and tools you use to validate and clean data, as well as any frameworks you follow.

Example

“I implement data validation checks at multiple stages of the data pipeline, using tools like Apache Airflow for orchestration. Additionally, I regularly conduct data audits to identify and rectify any discrepancies.”

4. What experience do you have with API development? Can you provide an example?

This question assesses your ability to develop and maintain APIs, which is a key responsibility in the role.

How to Answer

Share a specific example of an API you developed, including the technologies used and the impact it had on the project.

Example

“I developed a RESTful API using Flask for a data analytics platform, which allowed users to query large datasets efficiently. This API reduced data retrieval times by 30%, significantly improving user experience.”

5. Can you explain your understanding of vehicle communication protocols like CAN and UDP?

This question tests your knowledge of specific protocols relevant to the aerospace industry.

How to Answer

Provide a brief overview of each protocol and discuss any relevant experience you have working with them.

Example

“I have worked with CAN protocol in automotive applications, where I used it for real-time data transmission between vehicle components. I also have experience with UDP for streaming data in applications where speed is critical, such as telemetry data from aircraft.”

Data Management and Analysis

1. How do you approach data visualization, and what tools do you prefer?

This question evaluates your ability to present data effectively.

How to Answer

Discuss your preferred tools and methodologies for data visualization, along with examples of how you’ve used them.

Example

“I prefer using Tableau for data visualization due to its user-friendly interface and powerful capabilities. In my last project, I created interactive dashboards that allowed stakeholders to explore test data in real-time, leading to more informed decision-making.”

2. Describe your experience with relational databases. How do you handle structured and unstructured data?

This question assesses your database management skills.

How to Answer

Explain your experience with relational databases and how you manage different data types.

Example

“I have extensive experience with PostgreSQL, where I designed schemas for structured data. For unstructured data, I often use NoSQL databases like MongoDB, allowing for flexible data storage and retrieval.”

3. What strategies do you use for optimizing database performance?

This question focuses on your ability to enhance database efficiency.

How to Answer

Discuss specific techniques you’ve implemented to improve performance, such as indexing or query optimization.

Example

“I regularly analyze query performance and implement indexing strategies to speed up data retrieval. In one instance, I optimized a slow-running query by restructuring it and adding appropriate indexes, which improved performance by over 50%.”

4. Can you explain how you would convert network packet data to a CSV format?

This question tests your technical skills in data transformation.

How to Answer

Outline the steps you would take to perform this conversion, including any tools or languages you would use.

Example

“I would use Python with libraries like Scapy to capture and parse network packets. After extracting the relevant fields, I would use Pandas to convert the data into a CSV format for easier analysis.”

5. How do you stay updated with the latest trends in data engineering and analytics?

This question assesses your commitment to continuous learning.

How to Answer

Share the resources you use to keep your skills current, such as online courses, blogs, or professional networks.

Example

“I regularly follow industry blogs and participate in webinars. I also take online courses on platforms like Coursera to learn about new tools and technologies, ensuring I stay ahead in the rapidly evolving field of data engineering.”