Brown & Brown Insurance Data Engineer Interview Questions + Guide in 2025

Brown & Brown Insurance is a leading independent insurance intermediary that delivers a variety of insurance and reinsurance products and services to clients across various sectors, including corporate, public entities, and individuals.

As a Data Engineer at Brown & Brown, you will play a crucial role in developing, automating, and managing cloud-based data capabilities that are integral to the company's overall data strategy and architecture. Your responsibilities will include engineering and supporting automated Azure cloud-based data solutions, creating and managing Infrastructure as Code (IaC) templates, and collaborating with cross-functional teams to enhance data performance and scalability. You will need a strong understanding of data pipeline implementation, data integration, transformation, and analytics, with a specific focus on Azure services such as Azure Data Factory and Azure Synapse Analytics. Additionally, proficiency in SQL, Python, and Java is essential, as well as the ability to mentor junior engineers and provide support to various delivery teams.

The ideal candidate will exhibit a passion for learning and adapting to new technologies, showcasing strong problem-solving skills and the ability to thrive in a fast-paced environment. This guide will equip you with insights into the role and the specific skills that Brown & Brown values, helping you prepare effectively for your interview and stand out as a candidate.

The interview process for a Data Engineer at Brown & Brown Insurance is structured to assess both technical skills and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different aspects of the candidate's qualifications and experience.

1. Initial HR Screening

The process begins with a brief phone interview with a recruiter from the HR department. This initial screening lasts about 30 minutes and focuses on understanding your background, experience, and motivations for applying to Brown & Brown. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role.

2. Technical Interview with Hiring Manager

Following the HR screening, candidates will have a technical interview with the hiring manager. This session is more in-depth and typically lasts around 45 minutes to an hour. The hiring manager will assess your technical expertise, particularly in areas such as SQL, cloud-based data solutions, and data pipeline development. Expect to discuss your previous projects and how they relate to the responsibilities of the Data Engineer role.

3. Peer Interviews

Candidates who progress past the hiring manager interview will participate in two additional interviews with peers from the data engineering team. These interviews focus on collaboration, problem-solving skills, and your ability to work within a team. They may also include technical questions that require you to demonstrate your knowledge of data engineering best practices and tools, such as Azure Data Factory and Python.

4. Final Interview

The final step in the interview process is typically a wrap-up discussion with senior leadership or a panel interview. This session may cover behavioral questions to gauge your alignment with the company’s values and culture. It’s also an opportunity for you to ask questions about the team dynamics, ongoing projects, and future opportunities within the company.

Throughout the interview process, candidates are encouraged to showcase their creativity, problem-solving abilities, and passion for data engineering.

Next, let’s explore the specific interview questions that candidates have encountered during this process.

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

Understand the Company Culture

Brown & Brown Insurance emphasizes a meritocratic culture that rewards self-starters and those committed to customer satisfaction. Familiarize yourself with the company's values and how they align with your own work ethic. Be prepared to discuss how your past experiences reflect a commitment to these values, particularly in terms of collaboration and problem-solving.

Prepare for Technical Proficiency

Given the role's focus on cloud-based data capabilities, ensure you have a strong grasp of Azure technologies, SQL, and Python. Brush up on your knowledge of data pipelines, data integration, and data transformation processes. Be ready to discuss specific projects where you implemented these technologies, highlighting your hands-on experience and problem-solving skills.

Showcase Your Problem-Solving Skills

The interview process may include scenarios where you need to demonstrate your analytical thinking and creativity. Prepare examples from your past work where you identified a problem, proposed a solution, and successfully implemented it. This will illustrate your ability to think critically and adapt in a fast-paced environment.

Emphasize Collaboration and Communication

As a Data Engineer, you will work closely with various teams, including operations and delivery teams. Highlight your experience in cross-functional collaboration and your ability to communicate complex technical concepts to non-technical stakeholders. Prepare to discuss how you have built strong relationships in previous roles and how you can contribute to a collaborative team environment at Brown & Brown.

Be Ready for Behavioral Questions

Expect behavioral interview questions that assess your adaptability and project ownership. Use the STAR (Situation, Task, Action, Result) method to structure your responses. This will help you convey your experiences clearly and effectively, demonstrating your fit for the role.

Prepare for a Multi-Stage Interview Process

The interview process may involve multiple stages, including discussions with HR, hiring managers, and peers. Approach each stage with the same level of preparation and professionalism. Be ready to ask insightful questions about the team dynamics and the specific challenges the department is facing.

Show Enthusiasm for Continuous Learning

Brown & Brown values individuals who are passionate about learning new technologies. Be prepared to discuss how you stay updated with industry trends and your commitment to professional development. Mention any relevant certifications or courses you are pursuing, especially those related to Azure or data engineering.

Follow Up Thoughtfully

After the interview, send a personalized thank-you note to your interviewers. Express your appreciation for the opportunity to learn more about the team and reiterate your enthusiasm for the role. This small gesture can leave a positive impression and reinforce your interest in the position.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Engineer role at Brown & Brown Insurance. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Brown & Brown Insurance. The interview will likely focus on your technical skills, experience with cloud-based data solutions, and your ability to work collaboratively within a team. Be prepared to discuss your past projects, your approach to problem-solving, and your understanding of data management principles.

Technical Skills

1. Can you explain your experience with cloud-based data platforms, particularly Azure?

This question assesses your familiarity with cloud technologies and your hands-on experience in implementing data solutions.

How to Answer

Discuss specific projects where you utilized Azure services, detailing the architecture and technologies you employed.

Example

“I have worked extensively with Azure Data Factory and Azure Databricks to build ETL pipelines. In my last project, I designed a data lake architecture that integrated various data sources, enabling real-time analytics for our business intelligence team.”

2. Describe your experience with SQL and how you have used it in your previous roles.

SQL proficiency is crucial for a Data Engineer, and this question evaluates your ability to manipulate and query data.

How to Answer

Provide examples of complex queries you’ve written, including any optimizations you made for performance.

Example

“In my previous role, I wrote complex SQL queries to extract insights from large datasets. I optimized these queries by using indexing and partitioning, which improved performance by 30%.”

3. What is Infrastructure as Code (IaC), and how have you implemented it in your projects?

This question tests your understanding of IaC and its application in data engineering.

How to Answer

Explain the concept of IaC and provide a specific example of how you used it to automate deployments.

Example

“I implemented IaC using Azure Resource Manager templates to automate the deployment of our data infrastructure. This approach reduced deployment time by 50% and minimized human error.”

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

Data quality is critical in data engineering, and this question assesses your approach to maintaining it.

How to Answer

Discuss the strategies and tools you use to monitor and validate data quality throughout the pipeline.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Great Expectations. Additionally, I set up alerts for any anomalies detected in the data flow, ensuring immediate action can be taken.”

5. Can you describe a challenging data engineering problem you faced and how you resolved it?

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

How to Answer

Share a specific challenge, the steps you took to address it, and the outcome.

Example

“I faced a challenge with data latency in our ETL process. I analyzed the bottlenecks and restructured the pipeline to use parallel processing, which reduced the data load time from hours to minutes.”

Collaboration and Communication

1. How do you approach collaboration with cross-functional teams?

This question assesses your teamwork and communication skills.

How to Answer

Discuss your experience working with different teams and how you ensure effective communication.

Example

“I regularly hold meetings with stakeholders from analytics and product teams to align on data requirements. I also use collaborative tools like JIRA to track progress and ensure transparency.”

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

This question evaluates your ability to communicate complex ideas clearly.

How to Answer

Provide an example of a situation where you successfully conveyed technical information to a non-technical audience.

Example

“I once presented our data architecture to the marketing team. I used visual aids and analogies to explain how data flows through our systems, which helped them understand how to leverage data for their campaigns.”

3. How do you handle feedback from peers or stakeholders?

This question assesses your receptiveness to feedback and your ability to adapt.

How to Answer

Discuss your approach to receiving and implementing feedback in your work.

Example

“I view feedback as an opportunity for growth. After receiving input on a data model I designed, I took the time to understand the concerns and made adjustments that improved the model’s efficiency.”

4. Can you give an example of how you mentored a junior team member?

This question evaluates your leadership and mentoring skills.

How to Answer

Share a specific instance where you provided guidance to a less experienced colleague.

Example

“I mentored a junior data engineer by pairing with them on a project. I guided them through the process of building a data pipeline and provided resources for them to learn independently, which boosted their confidence and skills.”

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

This question assesses your time management and organizational skills.

How to Answer

Explain your method for prioritizing tasks and managing deadlines.

Example

“I use a combination of project management tools and regular check-ins with my team to prioritize tasks based on urgency and impact. This approach helps me stay organized and focused on delivering high-quality work on time.”