Better Data Engineer Interview Questions + Guide in 2025

Better is a company focused on leveraging technology to improve the homeownership experience for consumers.

As a Data Engineer at Better, you'll play a pivotal role in the management and enhancement of the company's data platform. Your key responsibilities will include overseeing the data pipeline and data warehousing infrastructure, ensuring data quality, and providing actionable insights to stakeholders. You will work closely with both technical and business teams to analyze and improve data processing efficiencies, utilizing tools such as SQL and Python. Additionally, you'll be expected to mentor junior engineers, conduct code reviews, and run agile rituals, emphasizing collaboration and communication within cross-functional teams.

Successful candidates will possess strong technical skills in Python and SQL, as well as experience with data warehousing and pipeline management. A great fit for this role is someone who thrives in a fast-paced environment, can navigate complexities with ease, and demonstrates a commitment to continuous improvement and learning. Your ability to articulate technical concepts clearly and effectively will be crucial in collaborating with various stakeholders across the organization.

This guide will help you articulate your experiences and showcase your skills effectively, ensuring you are well-prepared for a challenging interview process at Better.

The interview process for a Data Engineer at Better is structured to assess both technical and interpersonal skills, reflecting the dynamic and fast-paced environment of the company.

1. Initial HR Screening

The process begins with an initial screening conducted by an HR representative. This typically lasts around 30 minutes and focuses on your background, experience, and motivation for applying to Better. The recruiter will also gauge your communication skills and cultural fit within the organization. Expect to discuss your resume and any relevant projects or experiences that align with the role.

2. Technical Interviews

Following the HR screening, candidates usually undergo a series of technical interviews, often comprising three rounds. Each round lasts approximately 30 minutes and may involve different stakeholders, including team leads and managers. These interviews will focus on your proficiency in SQL, Python, and data pipeline management. You may be asked to solve problems related to data optimization, service stability, and high availability, as well as demonstrate your understanding of data warehousing concepts and tools.

3. Behavioral and Situational Interviews

In addition to technical assessments, candidates will participate in behavioral interviews. These sessions aim to evaluate your problem-solving abilities, collaboration skills, and how you handle challenging situations. Expect questions that explore your past experiences, such as how you prioritize tasks under tight deadlines or how you mentor junior engineers. The interviewers may also present hypothetical scenarios to assess your decision-making process and leadership qualities.

4. Final Interview with Leadership

The final stage typically involves a conversation with senior leadership or the hiring manager. This interview may delve deeper into your strategic thinking and alignment with Better's mission. You might discuss your long-term career goals and how you envision contributing to the company's data initiatives. This is also an opportunity for you to ask questions about the team dynamics and the company's future direction.

As you prepare for your interviews, consider the specific skills and experiences that will showcase your fit for the Data Engineer role at Better. Next, we will explore the types of questions you might encounter during the interview process.

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

Communicate Your Passion and Fit

During the interview, it's crucial to articulate your genuine interest in the Data Engineer role at Better. Be prepared to explain how your background aligns with the responsibilities of managing data pipelines and warehousing. Highlight specific experiences that demonstrate your ability to take ownership of projects and improve data quality. This will not only showcase your technical skills but also your commitment to contributing to the team and the company's mission.

Prepare for a Fast-Paced Environment

Better is known for its rapid pace and dynamic work environment. Be ready to adapt quickly during the interview process, as you may encounter unexpected changes, such as meeting different stakeholders or managers. Show your flexibility and ability to thrive under pressure by discussing past experiences where you successfully navigated similar situations. This will demonstrate that you can handle the chaotic nature of the role.

Showcase Your Technical Proficiency

Given the emphasis on SQL and algorithms, ensure you are well-versed in these areas. Brush up on SQL optimization techniques, including query plan analysis and window functions. Be prepared to discuss your experience with data processing tools like Airflow and your familiarity with major BI stacks. You may be asked to solve technical problems or provide insights into your approach to data engineering challenges, so practice articulating your thought process clearly.

Emphasize Collaboration and Communication Skills

As a Data Engineer, you will need to interface with both technical and business stakeholders. Highlight your experience in cross-functional collaboration and your ability to communicate complex technical concepts in an understandable way. Prepare examples that illustrate your mentoring skills and how you've successfully worked with junior engineers or other teams to achieve common goals.

Be Ready for Behavioral Questions

Expect a mix of technical and behavioral questions. Prepare for scenarios that assess your problem-solving abilities, such as how you would prioritize tasks under tight deadlines or handle conflicts within a team. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your actions.

Understand the Company Culture

Familiarize yourself with Better's values and work culture. The company values communication and respect, so be sure to demonstrate these qualities during your interview. Show that you are not only a technical fit but also a cultural fit by discussing how your personal values align with those of the company. This will help you stand out as a candidate who is genuinely interested in contributing to the team.

Follow Up Thoughtfully

After the interview, consider sending a thoughtful follow-up email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your enthusiasm for the role and briefly mention any key points you may not have had the chance to elaborate on during the interview. This will leave a positive impression and keep you top of mind as they make their decision.

By following these tips, you can position yourself as a strong candidate for the Data Engineer role at Better. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Better. The interview process will likely focus on your technical skills, problem-solving abilities, and your capacity to communicate effectively with both technical and non-technical stakeholders. Be prepared to discuss your experience with data pipelines, SQL optimization, and your approach to collaboration within a team.

Technical Skills

1. Can you explain the process of optimizing a SQL query?

Understanding SQL optimization is crucial for a Data Engineer role, as it directly impacts data retrieval efficiency.

How to Answer

Discuss the various techniques you use for optimization, such as indexing, analyzing query plans, and avoiding unnecessary complexity in queries.

Example

“I typically start by examining the execution plan of the query to identify bottlenecks. I then look for opportunities to add indexes on frequently queried columns and simplify joins where possible. This approach has consistently improved query performance in my previous projects.”

2. Describe your experience with data pipeline management.

This question assesses your hands-on experience with data pipelines, which is a core responsibility of the role.

How to Answer

Highlight specific tools and frameworks you have used, such as Apache Airflow, and discuss how you ensure data quality and reliability.

Example

“I have managed data pipelines using Apache Airflow, where I set up workflows to automate data ingestion from various sources. I implemented monitoring to ensure data quality and established alerting mechanisms for any failures, which significantly reduced downtime.”

3. How do you ensure data durability and consistency in your data warehouse?

This question evaluates your understanding of data integrity principles.

How to Answer

Discuss the strategies you employ to maintain data durability, such as transaction management and backup processes.

Example

“I ensure data durability by implementing ACID properties in my transactions and regularly backing up the data warehouse. Additionally, I use version control for schema changes to maintain consistency across different environments.”

4. What is your experience with stream processing pipelines?

Stream processing is becoming increasingly important, and this question gauges your familiarity with real-time data processing.

How to Answer

Mention any frameworks you have used, such as Apache Kafka or Spark Streaming, and describe a project where you implemented stream processing.

Example

“I have worked with Apache Kafka to build a stream processing pipeline that ingests real-time data from IoT devices. This allowed us to process and analyze data on-the-fly, providing immediate insights to our stakeholders.”

5. Can you discuss a challenging data engineering problem you faced and how you solved it?

This question assesses your problem-solving skills and ability to handle complex situations.

How to Answer

Provide a specific example that highlights your analytical skills and technical expertise.

Example

“In a previous role, we faced significant latency issues in our data pipeline. I conducted a thorough analysis and discovered that a particular transformation step was causing delays. By optimizing the code and parallelizing the processing, I was able to reduce the latency by over 50%.”

Collaboration and Communication

1. How do you prioritize tasks when managing multiple data projects?

This question evaluates your time management and prioritization skills.

How to Answer

Discuss your approach to assessing project urgency and importance, and how you communicate with stakeholders.

Example

“I prioritize tasks based on project deadlines and stakeholder impact. I maintain open communication with my team and stakeholders to ensure alignment on priorities, which helps in managing expectations effectively.”

2. Describe a time when you had to explain a technical concept to a non-technical stakeholder.

This question assesses your communication skills and ability to bridge the gap between technical and non-technical teams.

How to Answer

Provide an example that demonstrates your ability to simplify complex concepts.

Example

“I once had to explain the importance of data quality to a marketing team. I used analogies related to their work, illustrating how poor data could lead to misguided campaigns. This helped them understand the value of our data processes and fostered better collaboration.”

3. How do you handle conflicts within a team?

This question evaluates your interpersonal skills and ability to maintain a positive team dynamic.

How to Answer

Discuss your approach to conflict resolution and maintaining team morale.

Example

“When conflicts arise, I believe in addressing them directly and constructively. I facilitate open discussions where team members can express their viewpoints, and I work towards finding a compromise that aligns with our project goals.”

4. How do you mentor junior data engineers?

This question assesses your leadership and mentoring abilities.

How to Answer

Discuss your approach to mentoring and the importance of knowledge sharing.

Example

“I take an active role in mentoring junior engineers by conducting regular code reviews and providing constructive feedback. I also encourage them to ask questions and engage in pair programming sessions, which helps them grow their skills in a supportive environment.”

5. What strategies do you use to ensure effective collaboration with cross-functional teams?

This question evaluates your ability to work with diverse teams.

How to Answer

Discuss your methods for fostering collaboration and communication across different teams.

Example

“I prioritize regular check-ins and updates with cross-functional teams to ensure everyone is aligned on project goals. I also use collaborative tools to share progress and gather feedback, which helps in building strong working relationships.”