Global Atlantic Financial Group Data Engineer Interview Questions + Guide in 2025

Global Atlantic Financial Group is a leader in the U.S. life insurance and annuity industry, committed to innovation and collaboration to best serve its clients.

As a Data Engineer at Global Atlantic, you will be responsible for designing and optimizing data architecture and pipelines that support various cross-functional teams. Your key responsibilities will include developing enterprise-level data solutions using advanced technologies such as AWS Glue, Lambda, and Spark, as well as crafting machine learning algorithms to meet business objectives. You will engage closely with business leaders, data analysts, and IT teams to drive the organization's data strategy initiatives forward.

To excel in this role, you will need a strong foundation in SQL and algorithms, with practical experience in Python and analytical programming. Your ability to solve complex business challenges using technology, along with excellent communication and collaboration skills, will set you apart. A background in both traditional and big data environments, coupled with a mastery of data management and BI architecture, is essential. Understanding modern software development life cycle principles and maintaining compliance with industry standards will be critical to your success.

This guide will prepare you for a successful interview by equipping you with a robust understanding of the key responsibilities, skills, and company culture that define the Data Engineer role at Global Atlantic Financial Group.

The interview process for the Data Engineer role at Global Atlantic Financial Group is structured to assess both technical expertise and cultural fit within the organization. Here’s what you can expect:

1. Initial Screening

The first step in the interview process is an initial screening, typically conducted by a recruiter. This 30-minute phone call focuses on your background, experience, and motivation for applying to Global Atlantic. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you understand the expectations and responsibilities.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment. This may be conducted via a video call with a senior data engineer or a technical lead. During this session, you will be evaluated on your proficiency in SQL, Python, and data architecture principles. Expect to solve problems related to data pipeline architecture, ETL processes, and possibly demonstrate your understanding of machine learning algorithms. This assessment is crucial as it gauges your technical skills and ability to apply them in real-world scenarios.

3. Behavioral Interview

After successfully passing the technical assessment, candidates will participate in a behavioral interview. This round typically involves multiple interviewers, including team members and managers. The focus here is on your past experiences, teamwork, and how you handle challenges. Be prepared to discuss specific instances where you demonstrated leadership, problem-solving, and collaboration skills, as these are essential for the role.

4. Onsite Interview

The final stage of the interview process is an onsite interview, which may also be conducted virtually. This comprehensive round consists of several one-on-one interviews with various stakeholders, including data analysts, IT team members, and business leaders. Each interview will delve deeper into your technical capabilities, project experiences, and your approach to data management and architecture. You may also be asked to present a case study or a project you have worked on, showcasing your analytical skills and technical knowledge.

5. Final Review

After the onsite interviews, the hiring team will convene to discuss your performance across all rounds. They will evaluate not only your technical skills but also your fit within the company culture and your potential contributions to the team. If selected, you will receive an offer that includes details about compensation and benefits.

As you prepare for your interview, consider the specific skills and experiences that align with the expectations of the Data Engineer role at Global Atlantic. Next, let’s explore the types of questions you might encounter during the interview process.

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

Understand the Data Landscape

Before your interview, familiarize yourself with the data landscape at Global Atlantic Financial Group. Understand the types of data they work with, the tools they use (like AWS Glue, Lambda, Spark, and SQL), and how these technologies integrate into their data strategy. This knowledge will allow you to speak confidently about how your skills and experiences align with their needs.

Showcase Your Technical Proficiency

Given the emphasis on SQL, algorithms, and data architecture, be prepared to discuss your technical skills in detail. Highlight your experience with data pipeline architecture, ETL processes, and machine learning algorithms. Be ready to provide specific examples of projects where you utilized these skills, particularly in a collaborative environment. This will demonstrate your ability to contribute effectively to cross-functional teams.

Emphasize Problem-Solving Skills

Global Atlantic values individuals who can solve complex business problems using technology. Prepare to discuss instances where you identified a problem, analyzed data, and implemented a solution. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly articulate your thought process and the impact of your actions.

Communicate Effectively

Strong communication and collaboration skills are essential for this role. Practice articulating your thoughts clearly and concisely, especially when discussing technical concepts. Be prepared to explain complex data-related topics in a way that non-technical stakeholders can understand. This will showcase your ability to bridge the gap between technical and business teams.

Align with Company Culture

Global Atlantic emphasizes collaboration and innovation. During your interview, express your enthusiasm for working in a team-oriented environment and your commitment to continuous improvement. Share examples of how you have contributed to a positive team culture in previous roles, whether through mentorship, knowledge sharing, or leading initiatives.

Prepare for Behavioral Questions

Expect behavioral questions that assess your adaptability, leadership, and teamwork. Reflect on your past experiences and be ready to discuss how you’ve handled challenges, conflicts, or changes in project scope. Highlight your ability to remain organized and analytical under pressure, as these traits are crucial for a Data Engineer.

Stay Current with Industry Trends

Demonstrating knowledge of current trends in data engineering, machine learning, and cloud technologies will set you apart. Be prepared to discuss how you stay updated on industry developments and how you can apply this knowledge to benefit Global Atlantic. This shows your commitment to professional growth and your proactive approach to your career.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers about the team dynamics, ongoing projects, and the company’s future direction. This not only shows your interest in the role but also helps you gauge if the company aligns with your career goals and values.

By following these tips, you will be well-prepared to make a strong impression during your interview for the Data Engineer role at Global Atlantic Financial Group. Good luck!

Global Atlantic Financial Group Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Global Atlantic Financial Group. The interview will focus on your technical skills, particularly in data architecture, machine learning, and SQL, as well as your ability to collaborate with cross-functional teams. Be prepared to demonstrate your problem-solving abilities and your understanding of data management principles.

Technical Skills

1. Can you explain the architecture of a data pipeline you have designed in the past?

This question assesses your practical experience in designing data pipelines and your understanding of data flow.

How to Answer

Discuss the components of the pipeline, the technologies used, and the challenges faced during implementation. Highlight how you ensured data quality and efficiency.

Example

“I designed a data pipeline using AWS Glue and Lambda to automate data ingestion from various sources. The pipeline included ETL processes that transformed raw data into a structured format for analysis. I faced challenges with data consistency, which I addressed by implementing validation checks at each stage of the pipeline.”

2. What is your experience with AWS Glue and Lambda?

This question evaluates your familiarity with specific tools that are crucial for the role.

How to Answer

Provide specific examples of projects where you utilized AWS Glue and Lambda, detailing the functionalities you implemented.

Example

“I have used AWS Glue to create ETL jobs that extract data from S3, transform it using Python scripts, and load it into Redshift. I also leveraged AWS Lambda to trigger these jobs based on events, ensuring timely data processing.”

3. Describe a machine learning project you have worked on. What algorithms did you use?

This question gauges your experience with machine learning and your ability to apply algorithms to real-world problems.

How to Answer

Outline the project objectives, the data used, the algorithms implemented, and the outcomes achieved.

Example

“I worked on a project to predict customer churn using logistic regression and decision trees. I gathered historical customer data, performed feature engineering, and trained the models. The final model improved our retention strategy by identifying at-risk customers with 85% accuracy.”

4. How do you ensure data quality in your projects?

This question assesses your understanding of data integrity and quality assurance practices.

How to Answer

Discuss the methods you use to validate and clean data, as well as any tools or frameworks that assist in maintaining data quality.

Example

“I implement data validation rules at the ingestion stage to catch anomalies early. Additionally, I use automated testing frameworks to run quality checks on the data after transformation, ensuring that it meets the required standards before it is used for analysis.”

5. Can you explain the differences between RDBMS and non-RDBMS databases?

This question tests your knowledge of database management systems and their appropriate use cases.

How to Answer

Discuss the characteristics of both types of databases, including their strengths and weaknesses, and provide examples of when to use each.

Example

“RDBMS databases, like MySQL, are structured and use SQL for querying, making them ideal for transactional systems. Non-RDBMS databases, such as MongoDB, are more flexible and can handle unstructured data, which is beneficial for big data applications where schema may evolve over time.”

Data Management and Analytics

1. How do you approach data migration projects?

This question evaluates your experience and strategy in handling data migration.

How to Answer

Describe your methodology for planning, executing, and validating data migrations, including any tools you use.

Example

“I start by assessing the source and target systems to understand data structures. I then create a detailed migration plan that includes data mapping, transformation rules, and validation steps. After migration, I conduct thorough testing to ensure data integrity and completeness.”

2. What strategies do you use for optimizing SQL queries?

This question assesses your SQL proficiency and your ability to enhance performance.

How to Answer

Discuss specific techniques you employ to improve query performance, such as indexing, query restructuring, or using analytical functions.

Example

“I optimize SQL queries by analyzing execution plans to identify bottlenecks. I often use indexing on frequently queried columns and rewrite complex joins into subqueries to improve performance. For large datasets, I also leverage window functions to minimize data scans.”

3. Describe a time when you had to troubleshoot a data issue. What steps did you take?

This question evaluates your problem-solving skills and your approach to data-related challenges.

How to Answer

Outline the issue, the steps you took to diagnose it, and how you resolved it.

Example

“I encountered a data discrepancy in our reporting dashboard. I traced the issue back to an ETL job that was failing silently. I implemented logging to capture errors and discovered that a transformation step was incorrectly configured. After correcting the configuration, I reran the job and validated the results.”

4. How do you stay updated with the latest trends in data engineering?

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

How to Answer

Mention specific resources, communities, or courses you engage with to keep your skills current.

Example

“I regularly follow industry blogs, participate in webinars, and am an active member of data engineering forums. I also take online courses to learn about new tools and technologies, such as the latest features in AWS and advancements in machine learning.”

5. Can you explain the concept of data warehousing and its importance?

This question tests your understanding of data warehousing principles and their relevance to business intelligence.

How to Answer

Discuss the purpose of data warehousing, its architecture, and how it supports decision-making processes.

Example

“Data warehousing involves collecting and managing data from various sources to provide meaningful business insights. It allows for historical analysis and reporting, enabling organizations to make data-driven decisions. A well-structured data warehouse supports efficient querying and data retrieval, which is crucial for business intelligence initiatives.”