GlobalLogic Data Engineer Interview Questions + Guide in 2025

GlobalLogic is a leader in digital engineering, helping brands across the globe design and build innovative products, platforms, and digital experiences for the modern world.

The Data Engineer role at GlobalLogic focuses on creating and maintaining the technical infrastructure required for optimal data processing. Key responsibilities include developing and managing large-scale data systems, architecting data pipelines and ETL processes, and collaborating with business teams to rapidly prototype analytical solutions. Candidates should possess strong programming skills in Python, a deep understanding of data structures and algorithms, and experience with data warehousing and ETL processes. Familiarity with cloud technologies, particularly AWS, as well as tools like Airflow, Spark, and SQL, are critical for success in this role. Strong analytical skills, problem-solving abilities, and excellent communication skills are essential traits that align with GlobalLogic's values of collaboration and innovation.

This guide will help you prepare for your interview by providing insights into the expectations for the Data Engineer role at GlobalLogic, enabling you to present your experience and skills effectively.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at GlobalLogic. The interview will likely focus on your technical skills, experience with data systems, and problem-solving abilities. Be prepared to discuss your past projects in detail, as well as demonstrate your proficiency in relevant technologies.

Technical Skills

1. Can you explain the ETL process and its importance in data engineering?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it forms the backbone of data integration and management.

How to Answer

Discuss the stages of ETL, emphasizing how each step contributes to data quality and accessibility. Mention any tools or technologies you have used in ETL processes.

Example

“ETL is essential for transforming raw data into a usable format. In my previous role, I used Apache NiFi to extract data from various sources, transform it using Python scripts, and load it into our data warehouse. This process ensured that our analytics team had access to clean and structured data for their analyses.”

2. Describe your experience with data modeling and schema design.

Data modeling is a key responsibility for Data Engineers, and interviewers will want to know your approach to designing efficient data structures.

How to Answer

Explain the types of data models you have worked with (e.g., star schema, snowflake schema) and how you ensure they meet business requirements.

Example

“I have extensive experience in designing star schemas for our data warehouse, which optimized query performance for our BI tools. I collaborated with stakeholders to understand their reporting needs, ensuring that the data model supported efficient data retrieval.”

3. What is your experience with cloud data platforms, particularly AWS?

Given the emphasis on cloud technologies, interviewers will assess your familiarity with cloud data services.

How to Answer

Discuss specific AWS services you have used, such as S3, Redshift, or Glue, and how they fit into your data engineering projects.

Example

“I have worked extensively with AWS S3 for data storage and AWS Glue for ETL processes. In a recent project, I set up a data lake on S3, which allowed us to store both structured and unstructured data, and used Glue to automate our ETL workflows.”

4. How do you optimize SQL queries for performance?

SQL optimization is critical for ensuring efficient data retrieval, and interviewers will want to know your strategies.

How to Answer

Share techniques you use to improve query performance, such as indexing, query rewriting, or analyzing execution plans.

Example

“I regularly analyze execution plans to identify bottlenecks in my SQL queries. For instance, I optimized a slow-running report by adding appropriate indexes and rewriting the query to reduce the number of joins, which improved performance by over 50%.”

Programming and Tools

5. Can you describe a project where you used Python for data analysis?

Python is a key language for data engineering, and interviewers will want to hear about your practical experience.

How to Answer

Detail the project, the libraries you used (e.g., Pandas, NumPy), and the outcomes of your analysis.

Example

“In a recent project, I used Pandas to analyze customer behavior data. I wrote scripts to clean and transform the data, which allowed us to identify trends and make data-driven decisions that increased customer retention by 20%.”

6. What tools do you use for data orchestration, and why?

Data orchestration tools are essential for managing data workflows, and interviewers will want to know your preferences.

How to Answer

Discuss your experience with tools like Apache Airflow or AWS Step Functions, and explain why you prefer them.

Example

“I prefer using Apache Airflow for data orchestration due to its flexibility and ease of use. I have set up complex workflows that automate our ETL processes, allowing for better monitoring and error handling.”

7. How do you ensure data quality in your pipelines?

Data quality is paramount in data engineering, and interviewers will assess your strategies for maintaining it.

How to Answer

Explain the methods you use to validate and clean data, as well as any tools that assist in this process.

Example

“I implement data validation checks at various stages of my ETL pipelines. For instance, I use Great Expectations to define expectations for data quality, which helps catch issues early in the process and ensures that only clean data is loaded into our warehouse.”

8. Describe your experience with containerization and orchestration tools.

Containerization is increasingly important in data engineering, and interviewers will want to know your familiarity with these technologies.

How to Answer

Discuss your experience with Docker, Kubernetes, or similar tools, and how they have improved your workflows.

Example

“I have used Docker to containerize our data processing applications, which simplified deployment and scaling. Additionally, I have experience with Kubernetes for orchestrating these containers, allowing us to manage resources efficiently and ensure high availability.”

Problem-Solving and Collaboration

9. Tell me about a challenging data engineering problem you faced and how you solved it.

Interviewers want to assess your problem-solving skills and ability to handle complex situations.

How to Answer

Describe the problem, your approach to finding a solution, and the outcome.

Example

“Once, we faced a significant delay in our ETL process due to a sudden increase in data volume. I analyzed the bottlenecks and implemented parallel processing using PySpark, which reduced the processing time by 70% and ensured timely data availability for our analytics team.”

10. How do you collaborate with data scientists and other stakeholders?

Collaboration is key in data engineering, and interviewers will want to know how you work with others.

How to Answer

Discuss your communication strategies and how you ensure alignment with other teams.

Example

“I prioritize regular communication with data scientists to understand their data needs. I hold weekly meetings to discuss ongoing projects and gather feedback, which helps me tailor our data pipelines to better support their analyses.”

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

Understand Your Projects Deeply

Be prepared to discuss your past projects in detail. Interviewers at GlobalLogic are interested in your hands-on experience, particularly how you utilized specific technologies and the rationale behind your choices. Be ready to explain not just what you did, but why you did it that way, and what alternatives you considered. This demonstrates critical thinking and a deep understanding of your work.

Master the Technical Fundamentals

Brush up on your Python skills, especially with libraries like Pandas and NumPy, as well as SQL. Expect to encounter coding challenges that may involve string manipulation or data frame operations. Familiarize yourself with data modeling concepts and ETL processes, as these are crucial for a Data Engineer role. Additionally, understanding cloud technologies, particularly AWS services like S3, Glue, and EMR, will be beneficial.

Prepare for Behavioral Questions

GlobalLogic values collaboration and communication, so be ready to discuss how you've worked in teams, resolved conflicts, and contributed to a positive work environment. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on your role in team projects and how you adapted to challenges.

Showcase Your Problem-Solving Skills

Expect to face questions that assess your problem-solving abilities. Be prepared to walk through your thought process when tackling complex data challenges. Interviewers may present hypothetical scenarios related to data architecture or pipeline design, so practice articulating your approach to these problems clearly and logically.

Familiarize Yourself with Company Culture

GlobalLogic promotes a collaborative and flexible work environment. Highlight your adaptability and willingness to learn from others. Mention any experiences you have working in diverse teams or across different geographical locations, as this aligns with their global operations.

Ask Insightful Questions

Prepare thoughtful questions that reflect your interest in the role and the company. Inquire about the team dynamics, ongoing projects, or how the company approaches professional development. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.

Practice Coding Challenges

Given the technical nature of the role, practice coding challenges on platforms like LeetCode or HackerRank. Focus on problems that involve data manipulation, SQL queries, and Python programming. This will help you feel more confident during the technical assessment portion of the interview.

Emphasize Continuous Learning

GlobalLogic values professional development, so be sure to express your commitment to continuous learning. Discuss any recent courses, certifications, or technologies you’ve explored that are relevant to the role. This shows that you are proactive and dedicated to staying current in the fast-evolving field of data engineering.

By following these tips, you can present yourself as a well-rounded candidate who not only possesses the technical skills required for the Data Engineer role but also aligns with GlobalLogic's values and culture. Good luck!

The interview process for a Data Engineer position at GlobalLogic is structured to assess both technical skills and cultural fit. It typically consists of several rounds, each designed to evaluate different aspects of your expertise and experience.

1. Initial Recruiter Call

The process begins with a brief phone call with a recruiter. This conversation usually lasts around 30 minutes and focuses on your background, the role's responsibilities, and your salary expectations. The recruiter will also gauge your interest in the company and its culture, ensuring that you align with GlobalLogic's values.

2. Technical Assessment

Following the initial call, candidates are often required to complete a technical assessment. This may involve a coding challenge that tests your proficiency in Python, SQL, and data manipulation techniques. Expect to solve problems related to data structures, algorithms, and possibly some string manipulation tasks. This assessment is crucial as it demonstrates your ability to handle real-world data engineering challenges.

3. In-Depth Technical Interview

The next step typically involves a more in-depth technical interview, which may be conducted via video conferencing. During this round, you will discuss your previous projects in detail, including the technologies you used and the rationale behind your choices. Be prepared to explain how you approached specific challenges and what alternatives you considered. This interview may also include questions about data modeling, ETL processes, and cloud technologies, particularly AWS.

4. Behavioral Interview

In addition to technical skills, GlobalLogic places a strong emphasis on cultural fit. A behavioral interview will assess your soft skills, teamwork, and problem-solving abilities. Expect questions that explore how you handle conflicts, work in teams, and adapt to changing environments. This round is essential for determining how well you would integrate into the existing team dynamics.

5. Final Interview with Leadership

The final stage often involves a conversation with senior leadership or hiring managers. This interview may cover both technical and strategic aspects of the role, including your vision for data engineering and how you can contribute to the company's goals. It’s an opportunity for you to showcase your understanding of the industry and your long-term aspirations within the company.

As you prepare for these interviews, it's important to familiarize yourself with the types of questions that may be asked, particularly those that relate to your technical expertise and past experiences.

1. Given two strings, string1 and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

For the two strings, our correspondence must be between characters in the same position/index.

Example 1:

Input:

string1 = 'qwe'
string2 = 'asd'

string_map(string1, string2) == True

# q = a, w = s, and e = d

Example 2:

Input:

string1 = 'donut'
string2 = 'fatty'

string_map(string1, string2) == False
# cannot map two distinct characters to two equal characters

Example 3:

Input:

string1 = 'enemy'
string2 = 'enemy'

string_map(string1, string2) == True
# there exists a one-to-one correspondence between equivalent strings

Example 4:

Input:

string1 = 'enemy'
string2 = 'ymene'

string_map(string1, string2) == False
# since our correspondence must be between characters of the same index, this case returns 'False' as we must map e = y AND e = e

2. How does random forest generate the forest and why use it over logistic regression?

Explain how a random forest generates multiple decision trees to form a forest. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

3. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms and describe scenarios where bagging is preferred over boosting. Provide examples of the tradeoffs, such as variance reduction in bagging and bias reduction in boosting.

4. How would you evaluate and compare two credit risk models for personal loans?

1. Identify the type of model developed by the co-worker for loan approval.
2. Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.
3. List the metrics to track for measuring the new model’s success, such as accuracy, precision, recall, and AUC-ROC.

5. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

6. What are the key differences between classification and regression models?

Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

7. How would you design a function to detect anomalies in univariate and bivariate datasets?

How would you design a function to detect anomalies if given a univariate dataset? What if the data is bivariate?

8. What are the drawbacks of the given student test score data layouts, and how would you reformat them?

Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in “messy” datasets.

9. What is the expected churn rate in March for customers who bought a subscription since January 1st?

You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, calculate the expected churn rate in March for all customers who bought the product since January 1st.

10. How would you explain a p-value to a non-technical person?

Describe what a p-value is in simple terms for someone who is not familiar with technical or statistical concepts.

11. What are Z and t-tests, and when should you use each?

Explain Z and t-tests, their uses, their differences, and when to use one over the other.

12. What metrics would you use to determine the value of each marketing channel?

Given marketing channels and their costs for a B2B analytics dashboard company, identify key metrics to evaluate each channel’s value.

13. How would you determine the next partner card using customer spending data?

Using customer spending data, outline a method to identify the best partner for a new credit card offering.

14. How would you investigate if the redesigned email campaign led to the increase in conversion rate?

Analyze the impact of a redesigned email campaign on conversion rates. Determine if the increase is due to the campaign or other factors.

Here are a few tips to help you excel in the GlobalLogic software engineer interview process:

1. Brush Up on Your Technical Skills: Ensure you’re well-versed with the technologies listed in the job descriptions, such as Apache Spark, Scala, ETL processes, and SQL. Practicing coding challenges and reviewing data engineering fundamentals can be beneficial.

2. Understand Your Projects Thoroughly: Be prepared to discuss your past projects in-depth. You should be able to explain why you chose specific technologies, the challenges faced, and how you overcame them.

3. Effective Communication: Clear and concise communication is key. Make sure to articulate your thoughts well. Being able to explain complex technical concepts thoroughly is a valuable skill.

What is the average salary for a Data Engineer at GlobalLogic?

According to Glassdoor, GlobalLogic’s data engineers earn between $90K and $122K per year, with an average of $105K per year.

What does GlobalLogic offer to Data Engineers?

GlobalLogic offers exciting projects at the forefront of digital transformation, a collaborative environment with a diverse team, excellent work-life balance with flexible schedules, professional development through continuing education and certification, and competitive benefits, including health and life insurance, 401K plans, and PTO.

What skills are required for a Data Engineer position at GlobalLogic?

Candidates should possess expertise in Apache Spark, Scala, and Java, with strong programming skills in languages like Python. A solid understanding of big data technologies, ETL processes, and data warehousing principles is essential. Experience with cloud platforms like AWS or Azure, and familiarity with SQL and NoSQL databases, is also highly desirable.

What kinds of projects do Data Engineers work on at GlobalLogic?

Data Engineers at GlobalLogic work on innovative, market-defining products and digital experiences across various industries, including automotive, healthcare, financial services, and media. Projects range from building big data infrastructures and data pipelines to integrating machine learning and AI technologies into client solutions.

How does GlobalLogic support career development for Data Engineers?

GlobalLogic promotes professional growth through continuing education classes, technical and soft skills training, and professional certifications. Employees benefit from knowledge exchanges within the global organization, attending workshops, and collaborating with experts from different fields.

GlobalLogic offers a collaborative environment, exciting projects, and a strong commitment to work-life balance, making it an ideal workplace for those passionate about technology and innovation.

By joining the team, you’ll benefit from professional development opportunities, international exchanges, and excellent benefits — positioning you at the cutting edge of the digital transformation landscape.

To explore more interview guides from this company, visit the GlobalLogic main interview guide here at Interview Query.

Good luck with your interview!