FlexShopper is the largest online lease-to-own marketplace, offering a vast range of products from top brands with affordable weekly payments. Based in Boca Raton, Florida, FlexShopper enables customers to rent-to-own products within 12 months or less.

We are currently seeking an experienced Senior Data Engineer to join our revenue growth team. This full-time position involves leading the development and optimization of marketing and revenue growth data pipelines, which will be integrated into Looker Studio for advanced data visualization. Your role will encompass designing scalable data pipelines, ensuring data integrity, and generating actionable insights to guide our marketing strategies.

In this guide, we'll explore the interview process, common interview questions, and valuable tips to help you succeed. Let’s get started!

Submitting Your Application

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

In the case of data engineering roles, take-home assignments involving data pipelines, data augmentation, and visualization in Looker Studio are incorporated. Apart from these, your proficiency in Python, R, GCP, and BigQuery may also be assessed during the round.

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 Flexshopper office in Boca Raton, FL. Your technical prowess, including programming and data engineering 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 Senior Data Engineer role at Flexshopper.

Quick Tips For Flexshopper Senior Data Engineer Interviews

A few tips for acing your Flexshopper interview include:

• Understand the Business: Flexshopper’s core business revolves around lease-to-own services. Be sure to familiarize yourself with how their business model operates and how your data engineering skills can contribute to their marketing and revenue growth.
• Be Technically Proficient: Ensure that your skills in Python, R, SQL, and data visualization tools like Looker Studio are sharp. Review your knowledge of GCP and BigQuery, as these platforms are central to the role.
• Communicate Effectively: Practice explaining complex technical concepts to non-technical team members. Your ability to convey data insights in a clear and actionable manner is crucial for collaborating with marketing and other departments.

Typically, interviews at Flexshopper 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.

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

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

• How would you determine the next partner card using customer spending data? With access to customer spending data, outline a method to identify the best partner for a new credit card offering.

• How would you investigate if a redesigned email campaign led to increased conversion rates? Analyze a scenario where a new email campaign coincides with an increase in conversion rates. Determine if the increase is due to the 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.

• 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 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 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 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 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.

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

Q: What is FlexShopper and what do they do?

FlexShopper is the largest online lease-to-own marketplace, offering a vast selection of products from top brands. The platform is designed to help individuals who don't have the cash or credit available by offering affordable weekly payment plans, allowing them to rent-to-own products within 12 months or less.

Q: What does a Senior Data Engineer at FlexShopper do?

A Senior Data Engineer at FlexShopper leads development and optimization efforts for marketing and revenue growth-related data pipelines. Responsibilities include designing scalable data pipelines using Python/R and BigQuery, integrating data into Looker Studio for advanced visualization, and crafting insights to drive marketing and revenue growth strategies.

Q: What skills and qualifications are required for the Data Engineer position?

Candidates should have a Bachelor's degree or higher in Computer Science, Data Science, or a related field, with 5+ years of experience in data engineering. Proficiency in SQL, Python or R, and familiarity with GCP/BigQuery/Looker Studio are crucial. Strong analytical skills, problem-solving abilities, and excellent communication skills for cross-functional collaboration are also important.

Q: What is the work environment like at FlexShopper?

FlexShopper offers a fast-paced environment that emphasizes both business impact and the volume/frequency of output. The team values adaptability to evolving technologies and industry best practices. The role involves close collaboration with both technical and non-technical team members, making it an exciting and dynamic place to work.

Q: How can I prepare for an interview at FlexShopper?

To prepare for an interview at FlexShopper, research the company and the lease-to-own marketplace industry. Review your technical skills in SQL, Python, R, and familiarize yourself with GCP/BigQuery/Looker Studio. Use Interview Query to practice common interview questions and scenarios related to data engineering, ensuring you're ready to discuss your past experiences and technical projects.

Are you ready to take on the challenge of driving revenue growth through data-driven insights at FlexShopper, the leading online lease-to-own marketplace? As a Senior Data Engineer, you'll be at the forefront of advancing our data infrastructure, optimizing marketing strategies, and crafting actionable insights. Dive deeper into what this exciting role entails by exploring our FlexShopper Interview Guide, where you'll find a wealth of information, from interview questions to insights on FlexShopper's culture. At Interview Query, we provide the tools and resources to empower your journey towards acing the interview and making a significant impact in your career. Good luck, and we look forward to seeing you succeed!