Brex Data Engineer Interview Questions + Guide in 2025

Brex is an AI-powered spend platform that empowers companies to manage their finances confidently through integrated corporate cards, banking, and global payments, along with intuitive software for travel and expenses.

As a Data Engineer at Brex, you will play a crucial role in transforming raw data into actionable insights that drive business decisions across the organization. You will be responsible for designing, building, and maintaining scalable data models and pipelines that support the growing array of services and products at Brex. Your collaboration with Data Scientists, Software Engineers, and Business Analysts will be vital as you translate their data needs into robust and efficient data solutions. This role requires a deep understanding of data management best practices, ensuring the quality and reliability of data used across various analytics applications. Additionally, you will be instrumental in the design and maintenance of Core Data tables, serving as the high-quality, curated data source for diverse analytic applications.

The ideal candidate for this position will have a strong background in Data Engineering, with at least three years of experience in the field. Proficiency in modern data transformation tools like DBT, advanced SQL skills, and experience with data warehousing solutions such as Snowflake are essential. You should also be familiar with data workflow orchestration tools like Airflow and possess programming skills in Python. Communication skills are paramount, as you will need to act as a liaison between technical and non-technical teams, ensuring that data solutions align with business objectives.

This guide will help you prepare for your interview by providing insights into the expectations and challenges of the role, as well as highlighting the skills and experiences that are most relevant to Brex's data-driven culture.

The interview process for a Data Engineer position at Brex is structured to assess both technical skills and cultural fit, ensuring candidates are well-prepared to contribute to the company's data-driven initiatives. The process typically unfolds as follows:

1. Initial Recruiter Call

The first step is a 30-minute phone call with a recruiter. This conversation serves to introduce you to Brex, discuss your background, and gauge your interest in the role. The recruiter will also provide insights into the company culture and expectations for the position, as well as discuss your career goals and salary expectations.

2. Technical Phone Screen

Following the initial call, candidates usually participate in a technical phone screen with an engineer. This session lasts about an hour and focuses on coding and problem-solving skills. You may be asked to solve a coding problem collaboratively, often involving SQL or data manipulation tasks that reflect real-world scenarios relevant to the role.

3. Take-Home Assignment (Optional)

In some cases, candidates may be required to complete a take-home assignment. This task typically involves a data-related challenge that allows you to demonstrate your analytical skills and understanding of data engineering concepts. The assignment is designed to be completed in a reasonable timeframe, but candidates often report spending additional time to ensure quality.

4. Virtual Onsite Interview

The final stage of the interview process is a virtual onsite, which consists of multiple rounds, usually lasting around four hours. This includes: - Technical Interview: Focused on coding, debugging, and system design. You may be asked to build a small backend service or design a data pipeline, showcasing your ability to apply engineering principles to practical problems. - Behavioral Interview: This round assesses your alignment with Brex's core values. Expect questions that require you to share experiences and how they relate to the company's mission and culture. - Collaboration and Communication Assessment: You may engage in discussions with team members to evaluate how well you can bridge the gap between technical and non-technical stakeholders.

5. Feedback and Offer Stage

After the onsite interviews, candidates typically receive feedback from the interviewers. If successful, you will move on to the offer stage, where compensation and other employment terms will be discussed. The recruiter will provide clarity on the expected salary range and any additional benefits.

As you prepare for your interviews, it's essential to be ready for a mix of technical and behavioral questions that reflect the collaborative and innovative environment at Brex. Here are some of the types of questions you might encounter during the interview process.

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

Understand the Data Landscape at Brex

Before your interview, familiarize yourself with Brex's data ecosystem. Understand how data is utilized across various departments and how it informs decision-making. Be prepared to discuss how your experience aligns with Brex's focus on transforming raw data into actionable insights. Highlight any relevant projects where you collaborated with cross-functional teams, as this will demonstrate your ability to bridge the gap between technical and non-technical stakeholders.

Prepare for Technical Depth

Given the emphasis on practical skills over theoretical knowledge, focus on real-world applications of your technical expertise. Brush up on your SQL skills, particularly in data transformation and modeling, as well as your experience with tools like DBT, Snowflake, and Airflow. Be ready to discuss specific challenges you've faced in data engineering and how you overcame them. Expect to engage in discussions about data pipelines, ETL processes, and data quality management.

Showcase Your Problem-Solving Skills

During the interview, you may be presented with scenarios that require you to think critically and solve problems on the spot. Practice articulating your thought process clearly and methodically. Use the STAR (Situation, Task, Action, Result) method to structure your responses, especially when discussing past projects or challenges. This will help interviewers understand your approach to problem-solving and your ability to deliver results.

Emphasize Collaboration and Communication

Brex values strong communication skills and the ability to work collaboratively with various teams. Be prepared to share examples of how you've successfully collaborated with data scientists, analysts, and business units in the past. Highlight your experience in translating complex technical concepts into understandable terms for non-technical stakeholders. This will demonstrate your ability to act as a liaison between technical and non-technical teams, a key responsibility in the role.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your alignment with Brex's core values. Prepare anecdotes that reflect your work ethic, adaptability, and commitment to excellence. Consider how your personal values align with Brex's mission and culture, and be ready to discuss why you want to work there. Authenticity is key, so share genuine experiences that illustrate your character and professional journey.

Stay Engaged and Ask Insightful Questions

Throughout the interview process, maintain an engaging demeanor. Show enthusiasm for the role and the company. Prepare thoughtful questions that demonstrate your interest in Brex's data initiatives and future projects. Inquire about the team dynamics, the tools they use, and how they measure success in data-driven initiatives. This not only shows your interest but also helps you assess if Brex is the right fit for you.

Follow Up Professionally

After your interview, send a thank-you note to express your appreciation for the opportunity to interview. Reiterate your interest in the role and briefly mention a key point from your conversation that resonated with you. This will leave a positive impression and keep you top of mind as they make their decision.

By following these tips, you'll be well-prepared to showcase your skills and fit for the Data Engineer role at Brex. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Brex. The interview process will focus on your technical skills, problem-solving abilities, and how well you can collaborate with both technical and non-technical teams. Be prepared to discuss your experience with data modeling, SQL, ETL processes, and your approach to building scalable data solutions.

Technical Skills

1. Can you explain the ETL process and how you have implemented it in your previous projects?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is fundamental to data integration and management.

How to Answer

Discuss your experience with each stage of the ETL process, emphasizing any tools or technologies you used. Highlight specific projects where you successfully implemented ETL processes and the impact they had on data accessibility and quality.

Example

“In my previous role, I designed an ETL pipeline using Apache Airflow to extract data from various sources, transform it using Python scripts, and load it into a Snowflake data warehouse. This process improved data accessibility for our analytics team, reducing the time to generate reports by 30%.”

2. How do you ensure data quality and integrity in your data pipelines?

Data quality is essential for reliable analytics and decision-making.

How to Answer

Explain the methods you use to validate and clean data, such as implementing data validation checks, monitoring data quality metrics, and using automated testing frameworks.

Example

“I implement data validation checks at each stage of the pipeline, using tools like Great Expectations to ensure data quality. Additionally, I set up alerts for any anomalies detected in the data, allowing for quick remediation before it impacts downstream processes.”

3. Describe a challenging data modeling problem you faced and how you resolved it.

Data modeling is a key responsibility for a Data Engineer, and interviewers want to see your problem-solving skills.

How to Answer

Share a specific example of a complex data modeling challenge, detailing the steps you took to analyze the problem, the solution you implemented, and the results.

Example

“I once faced a challenge with a rapidly growing dataset that required a new schema design. I conducted a thorough analysis of the data access patterns and collaborated with stakeholders to create a star schema that improved query performance by 40%.”

4. What experience do you have with SQL, and can you provide an example of a complex query you wrote?

SQL proficiency is critical for data manipulation and analysis.

How to Answer

Discuss your experience with SQL, including the types of databases you’ve worked with. Provide a specific example of a complex query, explaining its purpose and the results it produced.

Example

“I have extensive experience with SQL, particularly in PostgreSQL. One complex query I wrote involved multiple joins and subqueries to analyze customer churn rates. This query helped identify key factors contributing to churn, leading to targeted retention strategies.”

5. How do you approach performance optimization in your data pipelines?

Performance optimization is vital for ensuring efficient data processing.

How to Answer

Discuss the techniques you use to optimize performance, such as indexing, partitioning, or using caching strategies. Provide examples of how these techniques improved pipeline efficiency.

Example

“I regularly analyze query performance and use indexing to speed up data retrieval. In one instance, I implemented partitioning on a large table, which reduced query execution time by over 50%, significantly improving the overall performance of our reporting system.”

Collaboration and Communication

1. How do you work with Data Scientists and Business Analysts to understand their data needs?

Collaboration is key in a Data Engineering role, and understanding stakeholder needs is essential.

How to Answer

Describe your approach to gathering requirements from stakeholders, including any tools or methods you use to facilitate communication and ensure alignment.

Example

“I schedule regular meetings with Data Scientists and Business Analysts to discuss their data needs. I use tools like JIRA to track requests and ensure that I’m prioritizing tasks that align with business objectives. This collaborative approach has led to more effective data solutions.”

2. Can you give an example of a time you had to explain a technical concept to a non-technical audience?

Being able to communicate complex ideas simply is important in a cross-functional environment.

How to Answer

Share a specific instance where you successfully explained a technical concept to a non-technical audience, focusing on your communication style and the outcome.

Example

“I once presented a new data pipeline to the marketing team. I used visual aids to illustrate how the pipeline worked and its benefits. By focusing on the business impact rather than the technical details, I was able to gain their support and ensure they understood how it would help their campaigns.”

3. Describe a time when you had a conflict with a team member and how you resolved it.

Conflict resolution skills are important for maintaining a collaborative work environment.

How to Answer

Provide a specific example of a conflict, detailing the situation, your approach to resolving it, and the outcome.

Example

“I had a disagreement with a colleague about the best approach to a data integration project. I suggested we hold a meeting to discuss our perspectives and gather input from other team members. This collaborative approach helped us find a compromise that combined both of our ideas, leading to a successful project.”

4. How do you prioritize your tasks when working on multiple projects?

Time management and prioritization are essential skills for a Data Engineer.

How to Answer

Discuss your approach to prioritizing tasks, including any frameworks or tools you use to manage your workload effectively.

Example

“I use the Eisenhower Matrix to prioritize my tasks based on urgency and importance. This helps me focus on high-impact projects while ensuring that I meet deadlines for ongoing tasks. I also communicate regularly with my team to align on priorities.”

5. Why do you want to work at Brex, and how do you see yourself contributing to our data team?

Understanding your motivation for joining the company and how you can add value is important for interviewers.

How to Answer

Express your enthusiasm for the company and the role, and highlight specific skills or experiences that align with Brex’s goals.

Example

“I admire Brex’s commitment to innovation and data-driven decision-making. I believe my experience in building scalable data pipelines and collaborating with cross-functional teams will allow me to contribute effectively to the data team and help drive impactful insights for the business.”