Popular Bank is a prominent financial institution known for its wide range of services and financial solutions across Puerto Rico, the United States, and the Virgin Islands. Our resilient and innovative employees are dedicated to making customer's dreams come true, while supporting communities with their diverse skills and experiences.

As a Data Engineer at Popular, you will play a crucial role within the Analytical Engineering & Enablement pillar. You will focus on data preprocessing, feature engineering, and ensuring smooth data movement to guide data-driven decision-making. Your responsibilities include mentoring, leading initiatives, and collaborating with multifaceted teams to deliver comprehensive data and analytics solutions.

Get ready for a rewarding career at Popular Bank. In this guide, Interview Query will walk you through the interview process, provide commonly asked questions, and offer valuable tips for acing your interview. Let's begin your journey toward joining our community of over 8,000 dedicated employees!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Popular Bank as a Data Engineer. Whether you were contacted by a Popular Bank 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 Popular Bank 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 Popular Bank 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 Popular Bank Data Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may involve topics such as data architecture, ETL pipelines, and SQL queries.

In the case of Data Engineer roles, take-home assignments regarding data preprocessing, feature engineering, and cleaning techniques are incorporated. Apart from these, your proficiency regarding data governance, security, and performance optimization may 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 Popular Bank office. Your technical prowess, including programming and data manipulation 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 Popular Bank.

Quick Tips For Popular Bank Data Engineer Interviews

You should plan to brush up on any technical skills and try as many Interview Query questions and mock interviews as possible. A few tips for acing your Popular Bank interview include:

• Understand Data Engineering Principles: Familiarize yourself with ETL processes, data warehousing, and data lifecycle management. Make sure you are comfortable discussing and solving problems related to data preprocessing, feature engineering, and data governance.
• Showcase Cloud Experience: Highlight your hands-on experience with cloud platforms like AWS, Azure, or Google Cloud. Be prepared to discuss specific services you have used and projects you have implemented.
• Emphasize Collaboration: Popular Bank values teamwork and cross-functional collaboration. Be ready to share examples of how you have successfully worked with diverse teams and stakeholders to deliver data solutions.

Typically, interviews at Popular Bank 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 potential issues. Suggest formatting changes to make the data more useful for analysis. Also, describe common problems in "messy" datasets.

• What metrics would you use to determine the value of each marketing channel? Given the marketing costs for different channels at a B2B analytics 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? Using customer spending data, outline the process to identify the most suitable partner for a new partner card, similar to Starbucks or Whole Foods chase credit cards.

• How would you investigate if the redesigned email campaign led to an increase in conversion rates? Given the fluctuating conversion rates before and after a new email campaign, describe how you would determine if the redesigned email journey caused the increase in conversion rates or if other factors were involved.

• 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 the 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.

• 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? 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.

• 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 since January 1st?

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

• What are Z and t-tests, and when should you use each? Describe what Z and t-tests are, their uses, differences, and when to 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 to form a forest. 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 two machine learning algorithms. Describe scenarios where bagging is preferred over boosting and vice versa. Provide examples of the tradeoffs between the two methods.

• What kind of model did the co-worker develop for loan approval? Identify the type of model used for determining loan approval based on customer inputs. Explain how to compare this model with another model predicting loan defaults, considering the monthly installment nature of personal loans. List metrics to track the success of the new model.

• What’s the difference between Lasso and Ridge Regression? Describe 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? Explain the fundamental differences between classification models and regression models, including their objectives, output types, and typical use cases.

What is the average salary for a Data Engineer at Popular Bank?

Q: What is the main focus of the Data Engineer position at Popular Bank?

The main focus of the Data Engineer position is to design, develop, and implement advanced analytical solutions. This includes data preprocessing, feature engineering, and ensuring smooth data movement to support informed decision-making and actionable insights. You will also delve into advanced statistical analysis and data transformation techniques to address significant business challenges.

Q: What skills and experiences are required for this position?

To qualify, you should have a bachelor’s degree in areas like computer science, Information Systems, or related fields, with a master’s degree being a plus. You should have a minimum of 15 years of experience with large-scale Data & Analytics platforms and at least 5 years in leading teams within ED&A. Proficiency with various data integration methodologies, cloud and on-premise data platforms such as Snowflake, AWS Redshift, and Databricks, and tools like Informatica, SQL, Python, and others, is crucial.

Q: What are some key responsibilities of the Data Engineer at Popular Bank?

Some key responsibilities include collaborating with cross-functional teams, establishing data quality and performance best practices, providing leadership within the Snowflake Center of Excellence, and promoting software engineering best practices. You will also focus on data quality, perform feature engineering, ensure compliance with data governance standards, and contribute to AI visualization and user-driven analytics initiatives.

Q: What is the working arrangement and location for this role?

Popular Bank offers hybrid or remote work schedules depending on your location. The primary regions for this position are North Carolina, Puerto Rico, Florida, and Illinois.

Q: How can I prepare for an interview for the Data Engineer position at Popular Bank?

To prepare, research Popular Bank’s services and values, and review common interview questions related to data engineering roles. Practice problem-solving and technical questions using a platform like Interview Query, and be ready to discuss your past experiences, technical projects, and how they align with Popular Bank’s objectives.

Embarking on a career as a Data Engineer at Popular Bank offers an extraordinary opportunity to leverage your expertise in data engineering and analytics within a vibrant financial institution dedicated to community service across Puerto Rico, the United States, and the Virgin Islands. With a robust focus on innovation and operational efficiency, Popular Bank promises a challenging yet rewarding professional journey for those passionate about data and analytics.

If you're eager to delve deeper into what it's like to be a part of our team, make sure to review our comprehensive Popular Bank Interview Guide, where you'll find insights and key interview questions tailored for the Data Engineer role. At Interview Query, we provide the resources you need to unlock your interview potential, equipping you with the knowledge and confidence to excel.

Prepare thoroughly, embrace continuous learning, and best of luck with your interview!