Milliman Data Engineer Interview Questions + Guide in 2025

Milliman is a respected global consultancy that specializes in actuarial consulting, healthcare financing, and data analytics, empowering organizations with data-driven solutions for improved decision-making and outcomes.

As a Data Engineer at Milliman, particularly within the IntelliScript team, you will play a crucial role in designing and implementing robust data platform solutions that drive the company's business objectives. Key responsibilities include creating efficient data architectures using technologies like Databricks and AWS, managing data pipelines, and ensuring compliance with data privacy standards. You will collaborate with cross-functional teams to optimize data processing, implement best practices, and drive data architecture decisions while focusing on both structured and unstructured healthcare data.

To excel in this role, candidates should possess extensive experience in data platform solutions, including Data Warehouses and Data Lakes, with a strong proficiency in SQL, Python, and cloud-based big data technologies. Familiarity with healthcare data standards such as FHIR and the ability to communicate complex technical concepts to non-technical stakeholders are essential traits. A collaborative mindset, customer-centric focus, and a "let's find a way to do it" attitude will set you apart as a strong candidate.

This guide will help you prepare for your interview by providing insights into the expectations for the Data Engineer role at Milliman, allowing you to showcase your skills and experiences effectively.

The interview process for a Data Engineer position at Milliman is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes several rounds of interviews, focusing on various competencies essential for the role.

1. Initial Screening

The process typically begins with an initial screening conducted by a recruiter. This conversation usually lasts about 30 minutes and serves as an opportunity for the recruiter to understand your background, skills, and career aspirations. During this call, you may also discuss the company culture and the specifics of the Data Engineer role. It's important to convey your enthusiasm for the position and demonstrate how your experience aligns with Milliman's values.

2. Technical Phone Interviews

Following the initial screening, candidates usually undergo a series of technical phone interviews. These interviews can range from two to three sessions, each lasting approximately 45 minutes to an hour. During these calls, you will be assessed on your technical expertise in areas such as SQL, Python, and data engineering concepts. Expect to answer questions related to data pipeline architecture, ETL processes, and your experience with tools like Databricks and AWS. Be prepared to discuss specific projects you've worked on and the challenges you faced.

3. Onsite Interviews

Candidates who successfully pass the technical phone interviews are typically invited for onsite interviews. This stage may consist of two to three rounds of interviews with various team members, including data engineers, data scientists, and project managers. Each interview will focus on different aspects of the role, such as data modeling, data governance, and collaboration with cross-functional teams. You may also be asked to solve real-world problems or case studies relevant to the healthcare data domain, showcasing your analytical and problem-solving skills.

4. Final Interview

The final interview often involves discussions with senior leadership or management. This round is less technical and more focused on assessing your fit within the company culture and your alignment with Milliman's core values. Expect to discuss your long-term career goals, how you handle ambiguity, and your approach to collaboration and mentorship. This is also an opportunity for you to ask questions about the team dynamics and the company's vision.

As you prepare for the interview process, it's essential to familiarize yourself with the types of questions that may be asked, particularly those that relate to your technical skills and experiences.

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

Understand the Company Culture

Milliman values collaboration, innovation, and transparency. Familiarize yourself with their core values and how they manifest in the workplace. Be prepared to discuss how your personal values align with Milliman's culture. Highlight experiences where you contributed to a team environment or drove innovative solutions, as this will resonate well with the interviewers.

Prepare for Technical Depth

Given the technical nature of the Data Engineer role, ensure you have a solid grasp of the technologies mentioned in the job description, such as Databricks, AWS, SQL, and ETL processes. Be ready to discuss your hands-on experience with these tools, including specific projects where you implemented data solutions. Practice articulating complex technical concepts in a way that is understandable to non-technical stakeholders, as this is a key aspect of the role.

Anticipate Behavioral Questions

Milliman's interview process may include behavioral questions that assess your problem-solving abilities and teamwork skills. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Prepare examples that showcase your ability to navigate challenges, collaborate with cross-functional teams, and drive projects to completion.

Be Ready for a Lengthy Process

Based on feedback from previous candidates, the interview process at Milliman can be lengthy, sometimes extending over several weeks. Stay patient and proactive; if you haven’t heard back in a while, consider following up with the recruiter. This demonstrates your continued interest in the position and can help keep your application top of mind.

Showcase Your Passion for Data

Milliman seeks candidates who are not only technically proficient but also passionate about data and its impact on healthcare. Be prepared to discuss why you are drawn to data engineering, particularly in the context of healthcare. Share insights on how data can drive better outcomes and your vision for leveraging data in innovative ways.

Prepare for Potential Curveballs

Some candidates have reported encountering unexpected or irrelevant questions during interviews. Stay calm and composed if faced with such questions. If you don’t know the answer, it’s okay to admit it and pivot to related knowledge or experiences. This shows humility and a willingness to learn, which are valued traits at Milliman.

Emphasize Continuous Learning

Milliman invests in employee development and encourages continuous learning. Highlight any recent courses, certifications, or self-directed learning you have undertaken to stay current in the field. Discuss how you plan to continue growing your skills and contributing to the team’s success.

Follow Up Thoughtfully

After your interview, send a personalized thank-you note to your interviewers. Reference specific topics discussed during the interview to reinforce your interest and engagement. This not only shows your appreciation but also keeps the lines of communication open.

By following these tips, you can present yourself as a well-prepared, culturally aligned, and technically capable candidate for the Data Engineer role at Milliman. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Milliman. The interview process will likely focus on your technical skills, experience with data platforms, and your ability to collaborate with cross-functional teams. Be prepared to discuss your past projects, the technologies you've used, and how you approach problem-solving in data engineering.

Data Platform and Architecture

1. Can you explain the differences between a Data Warehouse and a Data Lake?

Understanding the distinctions between these two data storage solutions is crucial for a Data Engineer, especially in a healthcare-focused environment.

How to Answer

Discuss the characteristics of each, including their purposes, data types they handle, and how they are structured. Highlight the importance of choosing the right solution based on business needs.

Example

“A Data Warehouse is optimized for structured data and is used for reporting and analysis, while a Data Lake can store both structured and unstructured data, making it more flexible for data exploration. In my previous role, I implemented a Data Lake to handle diverse healthcare data, which allowed for more comprehensive analytics.”

2. Describe your experience with Databricks and how you have utilized it in your projects.

Databricks is a key technology for this role, and your familiarity with it will be assessed.

How to Answer

Share specific projects where you used Databricks, focusing on the features you leveraged, such as Delta Lake or Spark.

Example

“I used Databricks to create a scalable ETL pipeline for processing patient data. By utilizing Delta Lake, I ensured data reliability and optimized performance, which significantly reduced our data processing time.”

3. What is Medallion Architecture, and how have you implemented it?

This architecture is relevant to the role, especially in managing data quality and lineage.

How to Answer

Explain the concept of Medallion Architecture and its stages (Bronze, Silver, Gold) and how it helps in organizing data for analytics.

Example

“I implemented Medallion Architecture in a healthcare project by first ingesting raw data into the Bronze layer, then cleaning and transforming it in the Silver layer, and finally aggregating it for reporting in the Gold layer. This approach improved our data quality and made it easier for analysts to access reliable data.”

4. How do you ensure data security and compliance when handling sensitive information?

Given the nature of healthcare data, this question is critical.

How to Answer

Discuss the protocols and best practices you follow to protect sensitive data, including encryption and access controls.

Example

“I ensure data security by implementing encryption for data at rest and in transit, and I regularly audit access controls to ensure only authorized personnel can access sensitive information. Additionally, I stay updated on HIPAA regulations to ensure compliance.”

5. Can you describe a challenging data pipeline you built and how you optimized it?

This question assesses your problem-solving skills and technical expertise.

How to Answer

Detail the challenges you faced, the solutions you implemented, and the results of your optimizations.

Example

“I built a data pipeline that initially struggled with performance due to high data volume. I optimized it by partitioning the data and using parallel processing in Spark, which improved the processing speed by 40%.”

ETL and Data Processing

1. What tools have you used for ETL processes, and how do you choose the right one?

Your experience with ETL tools is essential for this role.

How to Answer

Discuss the tools you’ve used, such as AWS Glue or Fivetran, and the criteria you consider when selecting an ETL tool.

Example

“I have used AWS Glue for ETL processes due to its serverless architecture, which allows for easy scaling. I choose ETL tools based on factors like data volume, transformation complexity, and integration capabilities with existing systems.”

2. Explain how you would handle data quality issues in a pipeline.

Data quality is crucial for reliable analytics, and your approach will be evaluated.

How to Answer

Describe the methods you use to monitor and ensure data quality throughout the ETL process.

Example

“I implement data validation checks at each stage of the ETL process, such as schema validation and duplicate detection. Additionally, I set up alerts for any anomalies, allowing for quick resolution of data quality issues.”

3. How do you approach performance tuning in data pipelines?

Performance tuning is vital for efficient data processing.

How to Answer

Discuss the techniques you use to optimize performance, such as indexing, partitioning, or caching.

Example

“I focus on optimizing SQL queries by using indexing and partitioning to reduce scan times. In one project, I reduced query execution time by 50% by rewriting inefficient queries and implementing caching strategies.”

4. Describe your experience with streaming data and how you have implemented it.

Streaming data is increasingly important, especially in real-time analytics.

How to Answer

Share your experience with streaming technologies and how you’ve applied them in projects.

Example

“I implemented a streaming solution using Apache Kafka to process real-time patient data. This allowed us to provide timely insights and alerts, significantly improving our response time to critical events.”

5. What strategies do you use for monitoring and maintaining data pipelines?

Monitoring is essential for ensuring the reliability of data pipelines.

How to Answer

Discuss the tools and practices you use for monitoring and maintaining data pipelines.

Example

“I use tools like Apache Airflow for scheduling and monitoring ETL jobs. I also implement logging and alerting mechanisms to quickly identify and resolve any issues that arise in the data pipeline.”

Data Modeling and Governance

1. Can you explain the importance of data governance in your projects?

Data governance is critical for maintaining data integrity and compliance.

How to Answer

Discuss the principles of data governance and how you’ve applied them in your work.

Example

“Data governance is essential for ensuring data quality and compliance with regulations. In my previous role, I established data stewardship roles and implemented data lineage tracking to maintain oversight of data usage and quality.”

2. Describe your experience with data modeling and the methodologies you prefer.

Your approach to data modeling will be assessed.

How to Answer

Share the methodologies you use, such as dimensional modeling or entity-relationship modeling, and provide examples.

Example

“I prefer using dimensional modeling for data warehouses as it simplifies complex queries and enhances performance. I recently designed a star schema for a healthcare analytics project, which improved query performance and user experience.”

3. How do you ensure that your data models align with business objectives?

Alignment with business goals is crucial for effective data solutions.

How to Answer

Discuss how you collaborate with stakeholders to ensure data models meet business needs.

Example

“I work closely with business analysts and stakeholders to gather requirements and understand their objectives. This collaboration ensures that the data models I design are aligned with their needs and can support their decision-making processes.”

4. What experience do you have with healthcare data standards like FHIR or HL7?

Knowledge of healthcare data standards is important for this role.

How to Answer

Discuss your experience with these standards and how you’ve applied them in your projects.

Example

“I have extensive experience with FHIR standards, having implemented them in a project to standardize patient data exchange between systems. This ensured interoperability and improved data accuracy across platforms.”

5. How do you handle changes in data requirements during a project?

Flexibility in adapting to changing requirements is key in data engineering.

How to Answer

Describe your approach to managing changes in data requirements and how you communicate with stakeholders.

Example

“I maintain open communication with stakeholders throughout the project. When changes arise, I assess the impact on the data model and pipeline, and I work collaboratively to implement the necessary adjustments while minimizing disruption.”