Northwestern Mutual Data Engineer Interview Questions + Guide in 2025

Northwestern Mutual is a leading financial services company dedicated to investing in its people and making positive impacts in the community.

As a Data Engineer, you will play a vital role in managing and enhancing the company's data infrastructure and analytics capabilities. Your primary responsibilities will include leading cloud migration efforts, specifically transitioning systems from on-premises to AWS while leveraging services such as RDS Aurora Postgres and AWS Glue. You will collaborate with internal teams to provide analytics-ready datasets and maintain AWS infrastructure, ensuring best practices for performance, security, and version control are followed. A strong background in relational databases and data warehousing is essential, along with proficiency in Python and SQL. Additionally, familiarity with DevOps practices and experience in building cloud infrastructure using Infrastructure as Code will be beneficial.

A great fit for this role will demonstrate excellent problem-solving skills, effective communication, and the ability to work collaboratively to support the organization's data needs. This guide will help you prepare effectively for your interview, enabling you to showcase your skills and align with Northwestern Mutual's values and expectations.

The interview process for a Data Engineer at Northwestern Mutual is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes various interview formats and focuses on relevant technical competencies.

1. Initial Recruiter Call

The process typically begins with a phone call from a recruiter. This initial conversation lasts about 30 minutes and serves as an opportunity for the recruiter to gauge your interest in the role and the company. During this call, you will discuss your background, skills, and motivations, as well as gain insights into the company culture and the specifics of the Data Engineer position.

2. Technical Assessment

Following the recruiter call, candidates may be required to complete a technical assessment. This assessment often includes questions related to SQL, Python, and data engineering concepts. The goal is to evaluate your proficiency in these areas and your ability to solve practical problems that a Data Engineer might encounter in their role.

3. Hiring Manager Interview

Next, candidates typically have a 30-minute interview with the hiring manager. This conversation is more focused on your past experiences and how they align with the responsibilities of the Data Engineer role. The hiring manager will assess your technical knowledge, problem-solving abilities, and how you approach teamwork and collaboration.

4. Onsite or Virtual Panel Interviews

The final stage of the interview process usually consists of a series of panel interviews, which can be conducted onsite or virtually. Candidates will meet with multiple team members, often in pairs, for approximately 45 minutes each. These interviews delve deeper into technical skills, including discussions on data pipeline management, cloud services (particularly AWS), and best practices in data engineering. Additionally, expect questions that explore your experience with ETL processes, data modeling, and your approach to ensuring data quality.

Throughout the interview process, Northwestern Mutual places a strong emphasis on cultural fit, so be prepared to discuss how you align with the company's values and how you work within a team environment.

As you prepare for your interviews, consider the specific technical skills and experiences that will be relevant to the questions you may encounter.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Northwestern Mutual. The interview process will focus on your technical skills, experience with data engineering practices, and your ability to work collaboratively within a team. Be prepared to discuss your past experiences, technical knowledge, and how you approach problem-solving in data-related projects.

Technical Skills

1. Can you explain the process of migrating data from on-premises systems to AWS?

This question assesses your understanding of cloud migration strategies and your experience with AWS services.

How to Answer

Discuss the steps involved in the migration process, including planning, data extraction, transformation, and loading (ETL), as well as any tools you have used, such as AWS Glue or Data Factory.

Example

“I have led several data migration projects where we transitioned from on-premises systems to AWS. The process typically starts with a thorough assessment of the existing data architecture, followed by designing a migration plan that includes data extraction using ETL tools like AWS Glue. We then transform the data to fit the new schema in AWS RDS and finally load it into the target database while ensuring data integrity and security throughout the process.”

2. What are the best practices for building data pipelines in the cloud?

This question evaluates your knowledge of cloud data engineering best practices.

How to Answer

Mention key practices such as modular design, error handling, monitoring, and using Infrastructure as Code (IaC) for deployment.

Example

“When building data pipelines in the cloud, I prioritize modular design to ensure each component can be independently managed and scaled. I also implement robust error handling and logging mechanisms to quickly identify and resolve issues. Additionally, I use Terraform for Infrastructure as Code to automate the deployment and management of our cloud resources, which enhances consistency and reduces manual errors.”

3. How do you ensure data quality in your ETL processes?

This question focuses on your approach to maintaining data integrity and quality.

How to Answer

Discuss techniques you use for data validation, cleansing, and monitoring throughout the ETL process.

Example

“To ensure data quality in my ETL processes, I implement validation checks at each stage of the pipeline. This includes verifying data types, checking for null values, and using data profiling tools to identify anomalies. I also set up automated monitoring to alert the team of any data quality issues in real-time, allowing us to address them promptly.”

4. Describe your experience with AWS services relevant to data engineering.

This question assesses your familiarity with AWS tools and services.

How to Answer

Highlight specific AWS services you have used, such as RDS, Glue, Lambda, and S3, and explain how you utilized them in your projects.

Example

“I have extensive experience with AWS services, particularly RDS for relational database management and AWS Glue for ETL processes. In my last project, I used Glue to automate data extraction and transformation tasks, which significantly reduced processing time. I also leveraged S3 for data storage and Lambda for serverless computing to trigger ETL jobs based on events.”

Collaboration and Problem-Solving

5. How do you handle project requirements from different stakeholders?

This question evaluates your communication and collaboration skills.

How to Answer

Explain your approach to gathering requirements, prioritizing tasks, and ensuring alignment among stakeholders.

Example

“I approach project requirements by first conducting meetings with stakeholders to gather their needs and expectations. I prioritize these requirements based on business impact and feasibility, ensuring that all parties are aligned on the project scope. Regular check-ins and updates help maintain transparency and allow for adjustments as needed.”

6. Can you provide an example of a data quality issue you encountered and how you resolved it?

This question assesses your problem-solving skills and experience with data quality challenges.

How to Answer

Share a specific example, detailing the issue, your analysis, and the steps you took to resolve it.

Example

“In a previous project, we discovered that a significant portion of our customer data had missing values, which affected our reporting accuracy. I conducted a root cause analysis and found that the issue stemmed from an upstream data source. I collaborated with the data source team to implement validation rules and created a data cleansing process to fill in the gaps, which improved our data quality significantly.”

7. How do you stay updated with the latest trends and technologies in data engineering?

This question gauges your commitment to continuous learning and professional development.

How to Answer

Discuss the resources you use, such as online courses, webinars, or industry publications, to keep your skills current.

Example

“I stay updated with the latest trends in data engineering by following industry blogs, participating in webinars, and taking online courses on platforms like Coursera and Udacity. I also engage with the data engineering community on forums like Stack Overflow and LinkedIn, where I can learn from others’ experiences and share my insights.”

8. What tools do you use for version control and collaboration in your projects?

This question assesses your familiarity with collaboration tools and practices.

How to Answer

Mention specific tools you have used, such as Git, GitLab, or other project management software, and explain how they facilitate collaboration.

Example

“I primarily use Git for version control, which allows me to track changes and collaborate effectively with my team. We also use GitLab for managing our CI/CD pipelines, which streamlines our deployment process. Additionally, we utilize project management tools like Jira to keep track of tasks and ensure everyone is aligned on project timelines and deliverables.”