Milliman Data Engineer interview questions & guide 2026

Preparation is most effective when you understand the patterns behind the questions. Based on real interview experiences at Milliman, the questions generally target core software engineering fundamentals, database theory, and logical problem-solving. Expect a mix of practical coding, query writing, and analytical reasoning.

SQL and Database Internals

Because Milliman processes massive volumes of structured financial and healthcare data, database performance and query optimization are highly scrutinized.

• Write a query to find the second highest transaction amount using a subquery, and then rewrite it using a window function. Explain the performance differences.
• How do you optimize a query that is running slowly on a table containing hundreds of millions of rows? Walk through your indexing strategy.

To succeed in the Milliman interview loop, you must demonstrate a balance of theoretical computer science knowledge and practical, hands-on data engineering capability.

Role-Related Knowledge – You must show a deep command of database engines, query optimization, and write clean, idiomatic Python. Your understanding of data structures, indexing, and distributed computing (like PySpark) should be practical, not just academic. Be ready to explain the trade-offs of the technologies you have used in past projects.

Problem-Solving & Logical Agility – Interviewers at Milliman place a premium on how you think. Whether you are writing a complex SQL query or solving a logical puzzle, they want to hear your thought process out loud. Break down complex requirements systematically, state your assumptions clearly, and validate your edge cases before declaring a solution complete.

Structural Code Design – Writing code that "just works" is not enough. You must write code that is modular, readable, and maintainable. Demonstrating a strong grasp of OOP principles and design patterns shows that you can contribute to their long-term, enterprise-grade codebases.

Communication and Domain Alignment – Milliman is a client-facing consulting and technology firm. You must be able to translate complex technical concepts into clear business impacts. Showing an interest in financial risk, actuarial concepts, or healthcare metrics will immediately differentiate you from other candidates.

The hiring process for a Data Engineer at Milliman is structured to thoroughly evaluate both your foundational technical capabilities and your analytical problem-solving skills. The process typically spans several weeks, and each round acts as a progressive filter.

Initially, the process begins with resume shortlisting, followed by an online aptitude and technical assessment. This initial test evaluates your logical reasoning, basic quantitative skills, and core programming fundamentals. Candidates who clear this threshold are contacted for an initial screening, which often includes a brief telephonic conversation focusing on basic SQL questions and your professional background.

Following the initial screen, you will enter the core technical evaluation phase. This consists of two to three intensive technical interviews. These rounds focus heavily on live coding, deep-dive database queries, Python fundamentals, and OOP concepts. You will also face logical puzzles designed to test your analytical thinking. The final stage is a managerial and HR round, which assesses your system design understanding, cultural fit, communication style, and alignment with the team's working dynamics.

The timeline above illustrates the standard progression from the initial online assessment through to the final managerial and HR evaluations. Most candidates find that the technical screens and live coding rounds require the highest concentration of preparation energy. While the process is rigorous, it is designed to ensure that you are fully prepared for the highly analytical environment at Milliman.

SQL & RDBMS Optimization

SQL is the lifeblood of Milliman’s data operations. You will be tested on your ability to write highly performant queries and your understanding of relational database management systems (RDBMS).

Be ready to go over:

• Query Optimization – How to read execution plans, utilize indexes (clustered vs. non-clustered), and avoid common pitfalls like table scans and unnecessary subqueries.
• Advanced Analytical Functions – Master window functions (ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()) and complex aggregations using GROUP BY and HAVING.

As a Data Engineer at Milliman, your day-to-day work is highly collaborative and directly impacts the accuracy of the firm’s analytical outputs. You will design, build, and maintain the data pipelines that ingest raw data from client systems, transform it into standardized formats, and load it into analytical databases and cloud data warehouses.

You will work closely with actuaries, data scientists, and software developers. Actuaries will provide the complex mathematical logic and business rules, and your job is to translate those requirements into scalable, automated, and fault-tolerant data pipelines. This requires a strong understanding of the underlying business logic, as a minor error in pipeline transformation can lead to major discrepancies in actuarial models.

Additionally, you will be responsible for continuous performance tuning and infrastructure monitoring. As data volumes grow, you will optimize database schemas, refactor legacy code, and migrate pipelines to modern cloud architectures. You will also play a key role in ensuring data security and compliance with international standards such as HIPAA and GDPR, implementing robust encryption, access controls, and data masking techniques.

To be competitive for the Data Engineer position at Milliman, you must present a strong technical foundation coupled with practical software engineering experience.

• Must-have Technical Skills – Deep expertise in writing and optimizing complex SQL queries, strong programming proficiency in Python, and a solid understanding of OOP concepts. Experience with relational databases (e.g., PostgreSQL, SQL Server) is essential.
• Nice-to-have Technical Skills – Hands-on experience with distributed computing frameworks like PySpark, familiarity with cloud data platforms (Azure, AWS, or Snowflake), and exposure to workflow orchestration tools like Apache Airflow.
• Experience Level – Typically, 2 to 5 years of professional experience in a data engineering or software engineering role, preferably within a data-intensive industry like finance, insurance, consulting, or healthcare.
• Soft Skills – Excellent verbal and written communication skills are critical. You must be comfortable explaining technical architectures to non-technical stakeholders (such as actuarial consultants) and working collaboratively across cross-functional teams.

Q: How difficult are the interviews at Milliman? A: The interviews are generally rated as average to difficult. The difficulty lies in the breadth of topics covered—you must be equally strong in logical puzzles, theoretical OOP, practical coding, and deep SQL database internals.

Q: What is the typical timeline from the first screen to an offer? A: The process can move relatively quickly once technical rounds begin, but overall timelines can stretch from 3 weeks to 2 months depending on the specific office location, team alignment, and scheduling availability. Proactive follow-ups with your recruiter are highly recommended.

Q: What differentiates a successful candidate from an average one? A: Successful candidates do not just write code; they design clean, scalable systems. They write modular, OOP-compliant Python, explain their SQL query optimization strategies with confidence, and demonstrate structured, vocalized thinking when solving logical puzzles.

Q: Does Milliman require domain knowledge in actuarial science or finance? A: While prior experience in insurance, healthcare, or finance is a significant advantage, it is not a strict prerequisite. Milliman values strong engineering fundamentals first; they will teach you the domain knowledge if your technical foundation is exceptional.

• Master the fundamentals of OOP: Do not just memorize definitions. Be prepared to write real Python classes that implement inheritance, polymorphism, and encapsulation during a live coding share.
• Think aloud during puzzles: Your interviewer is more interested in your problem-solving framework and how you handle ambiguity than they are in you getting the exact mathematical answer on your first try.
• Be ready for panel environments: Some technical rounds may involve multiple team members on the call simultaneously. Remain calm, address your answers to the group, and treat it as a collaborative design discussion.
• Prepare for SQL optimization deep dives: Expect questions on how databases actually execute your queries. Review indexing, join algorithms (hash, merge, nested loop), and execution plans before your technical rounds.

A Data Engineer position at Milliman offers a unique opportunity to work on highly complex, high-impact data challenges that directly shape global risk management and financial technologies. The work is intellectually stimulating, highly collaborative, and demands a level of precision that few other industries require.

As you prepare for your interviews, focus your efforts on mastering advanced SQL, refining your Python software engineering practices, and sharpening your logical deduction skills. Approaching each round with a structured, communicative, and problem-solving mindset will set you apart.

The salary data reflects the competitive compensation packages Milliman offers to secure top-tier engineering talent. When evaluating your offer, consider the full compensation structure, including base salary, performance bonuses, and the firm's robust retirement contributions.

To further refine your preparation, review additional real-world interview reports, detailed salary breakdowns, and interactive coding prep resources available on Dataford. With focused preparation and a clear understanding of what Milliman values, you are well-positioned to succeed in your upcoming interviews. Good luck!