Global Lending Services LLC (GLS) is an innovative leader in the auto finance industry, providing superior services to both consumers and dealers across the United States. With a dedicated focus on technology and customer satisfaction, GLS fosters a dynamic work environment that emphasizes growth and innovation.

As a Data Engineer at GLS, you'll be at the forefront of leveraging data to drive strategic decisions and optimize services. Candidates for this role need a strong foundation in data modeling, ETL processes, and big data technologies. Your responsibilities will include building scalable data pipelines, ensuring data integrity, and collaborating with cross-functional teams to deliver actionable insights.

This guide on Interview Query will help you prepare effectively for your interview at GLS. We’ll walk you through the interview process, share common Data Engineer interview questions, and provide essential tips to boost your chances of success. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Global Lending Services LLC as a Data Engineer. Whether you were contacted by a recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Global Lending Services Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Data Engineer role at Global Lending Services LLC usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around the company's data systems, ETL pipelines, and SQL queries.

Apart from these, your proficiency in data warehousing, data modeling, and scripting languages such as Python or Scala may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Global Lending Services office. Your technical prowess, including programming, data architecture design, and problem-solving capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Engineer role at Global Lending Services LLC.

Quick Tips For Global Lending Services LLC Data Engineer Interviews

• You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Global Lending Services LLC interview include:

• Understand Data Engineering Fundamentals: Ensure you have a strong grasp of data engineering principles, including ETL processes, data modeling, and SQL querying.

• Practice Problem Solving: Be prepared to tackle problem-solving questions that may require coding. Brush up on your skills in languages such as Python, Java, or Scala.
• Familiarize with Company Tools: Research and understand the data tools and technologies used by Global Lending Services, such as specific databases, data warehousing solutions, or cloud platforms.

Typically, interviews at Global Lending Services Llc vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

• What are the drawbacks of the given student test score datasets, and how would you reformat them? Analyze the provided student test score datasets for drawbacks. Suggest formatting changes to make the data more useful for analysis. Describe common problems in "messy" datasets.

• 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 metrics to evaluate the value of each channel.

• How would you determine the next partner card using customer spending data? Using customer spending data, outline the process to identify the best partner for a new partner card.

• How would you investigate if the redesigned email campaign led to the increase in conversion rate? Given an increase in new-user to customer conversion rate after a redesign, determine if the increase is due to the new email campaign or other factors.

• Write a function search_list to check if a target value is in a linked list. Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with keys value and next. If the linked list is empty, you'll receive None.

• Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product. Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

• Create a function digit_accumulator to sum every digit in a string representing a floating-point number. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

• Develop a function to parse the most frequent words used in poems. You're hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

• Write a function rectangle_overlap to determine if two rectangles overlap. You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

• 1. How would you design a function to detect anomalies in univariate and bivariate datasets? If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

• 2. What are the drawbacks of the given student test score data layouts? Assume you have data on student test scores in two layouts. What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

• 3. What is the expected churn rate in March for customers who bought subscriptions 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, what is the expected churn rate in March for all customers who bought the product since January 1st?

• 4. How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• 5. What are Z and t-tests, and when should you use each? What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

• How does random forest generate the forest and why use it over logistic regression? Explain the process of how random forest generates multiple decision trees and aggregates their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare the use cases for bagging and boosting algorithms. Provide examples of the tradeoffs, such as bagging reducing variance and boosting reducing bias.

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

• Identify the type of model developed by the co-worker for loan approval.
• Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installments.

• List the metrics to track for measuring the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

• 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.

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

Embarking on a career with Global Lending Services LLC as a Data Engineer is a valuable opportunity. If you want more insights about the company, check out our main Global Lending Services Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Global Lending Services’ interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Global Lending Services machine learning engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!