Capital One is a well-established financial firm known for its unique and innovative approach to finance and banking, ranking as the 9th largest bank in the U.S. with $468.8 billion in assets as of January 2024.

Loanpal is a world-positive lender dedicated to connecting billions of dollars in capital to homeowners wanting to upgrade to smart, energy-efficient homes. As a key player in the $350 billion+ home improvement industry, Loanpal empowers mission-driven businesses with fast, frictionless lending solutions, facilitating consumer cost savings through advanced lending technologies.

The Sr. Data Engineer position offers opportunities for remote work or work from the San Francisco Bay Area. Ideal candidates will have a proven background in building and optimizing data pipelines and architectures. Responsibilities include devising data solutions and supporting diverse teams within Loanpal to enhance the firm’s data infrastructure. If you are passionate about creating sustainable solutions and possess the required technical expertise, Loanpal invites you to join their innovative team. Visit Interview Query for more insights and interview preparation.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Loanpal as a Data Engineer. Whether you were contacted by a Loanpal 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 Loanpal 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 Loanpal Data Engineer 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 Loanpal Data Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Loanpal’s data systems, data pipeline architectures, and SQL queries.

In the case of data engineering roles, take-home assignments regarding data pipeline optimization, ETL processes, and data infrastructure design are incorporated. Apart from these, your proficiency against dealing with big data tools, AWS cloud services, and scripting languages 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 Loanpal office. Your technical prowess, including programming and data architecture 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 Loanpal.

Quick Tips For Loanpal Data Engineer Interviews

• Prepare on Big Data Tools: Be well-versed with big data tools like Hadoop, Spark, and Kafka. Familiarize yourself with AWS services such as EC2, EMR, RDS, and Redshift, as these are frequently used in the company.
• Showcase Data Engineering Projects: Highlight your experiences with building and optimizing data pipelines, architectures, and data sets in your past roles. Provide specific examples to illustrate your capabilities.
• Emphasize Cross-Functional Collaboration: Ensure that you demonstrate your experience working with cross-functional teams including software developers, database architects, data analysts, and data scientists. Loanpal values teamwork in achieving their data-related goals.

Typically, interviews at Loanpal 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 what Z and t-tests are, their uses, the differences between them, and the scenarios in which one should be used over the other.

• How would you reformat student test score data for better analysis? Given two datasets of student test scores, identify the drawbacks of their current organization, suggest formatting changes for better analysis, and describe common problems in "messy" datasets.

• What metrics would you use to determine the value of each marketing channel? Given data on marketing channels and their costs for a B2B analytics dashboard company, identify the metrics you would use to evaluate the value of each marketing channel.

• How would you determine the next partner card using customer spending data? With access to customer spending data, describe the process you would use to determine the next partner card for a company.

• How would you investigate if a redesigned email campaign led to an increase in conversion rates? Given an increase in new-user to customer conversion rates after a redesigned email journey, explain how you would investigate whether the increase was due to the 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 value and next keys. 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.

• 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?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? Assume you have data on student test scores in two different layouts. What are the drawbacks of these layouts? What formatting changes would you make to improve their usefulness for analysis? Additionally, describe common problems seen in “messy” datasets.

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

• 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?

• 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 how random forest creates multiple decision trees and combines 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 tradeoffs, such as bagging reducing variance and boosting improving accuracy but being more prone to overfitting.

• 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 metrics to track 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 differences between Lasso and Ridge Regression, focusing on their regularization techniques (L1 for Lasso and L2 for Ridge) and their impact on feature selection and model complexity.

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their objectives (predicting categories vs. continuous values), evaluation metrics, and typical use cases.

If you are excited about leveraging data to drive impactful changes in the home improvement industry and want to join a mission-driven company, Loanpal is the place for you. We provide a dynamic environment where your expertise in data engineering will be pivotal in creating cutting-edge solutions for a sustainable future. You'll have the opportunity to work with a diverse set of technologies and collaborate with cross-functional teams to optimize data systems and architecture.

If you want more insights about the company, check out our main Loanpal 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 Loanpal’s 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 Loanpal 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!