Wizeline is a global digital services company helping mid-size to Fortune 500 companies build, scale, and deliver high-quality digital products and services. We specialize in human-centered experiences, digital core modernization, and utilizing AI/ML and data to bring technology to the heart of their business.

If you're considering a Data Engineer position at Wizeline, expect to collaborate on designing and implementing high-performance and scalable data platforms. You’ll work within an Agile/Scrum framework alongside product owners, report developers, and business partners. Key responsibilities include refining data workflows, integrating data sources, and consistently monitoring system performance.

Here at Interview Query, we’ll walk you through the interview process, common interview questions, and provide some invaluable tips for your application journey. 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 Wizeline as a Data Engineer. Whether you were contacted by a Wizeline 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 Wizeline 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 Wizeline 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 Wizeline 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 Wizeline’s data systems, ETL pipelines, and SQL queries.

In the case of data engineer roles, take-home assignments regarding data engineering challenges, system design, and data pipeline creation may be incorporated. Apart from these, your proficiency against data modeling, big data technologies (e.g., Spark, Kafka), and cloud services (e.g., AWS) 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 Wizeline office. Your technical prowess, including programming and big data handling 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 Wizeline.

Quick Tips For Wizeline 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 Wizeline interview include:

• Understand Big Data Technologies: Wizeline positions use technologies like Spark and Kafka extensively. Ensure your knowledge and practical skills with these tools are up to date.
• Be Detail-Oriented with Cloud Services: Demonstrating your experience with AWS, Azure, or GCP can set you apart. Focus on specifics like data integration, storage solutions, and scalable architectures.
• Practice Behavioral Interviews: Prepare to discuss your previous projects, emphasizing your problem-solving skills, teamwork, and ability to work within an agile environment.

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

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to find the maximum number in a list of integers. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

• Create a function convert_to_bst to convert a sorted list into a balanced binary tree. Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The function should return a TreeNode holding the root of the binary tree.

• Write a function to simulate drawing balls from a jar. Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

• Develop a function can_shift to determine if one string can be shifted to become another. Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

• What are the drawbacks of having student test scores organized in the given layouts? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in "messy" datasets.

• How would you locate a mouse in a 4x4 grid using the fewest scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. Describe a strategy to find the mouse using the fewest number of scans.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in New York City and Charlotte. Describe the process for selecting Dashers (delivery drivers) and discuss whether the criteria for selection should be the same for both cities.

• What factors could bias Jetco's study on boarding times? Jetco, a new airline, has the fastest average boarding times according to a study. Identify potential factors that could have biased this result and explain what you would investigate further.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? A B2B SAAS company wants to test different subscription pricing levels. Design a two-week-long A/B test to evaluate a pricing increase and determine if it is a good business decision.

• How much should a ride-sharing app budget for a $5 coupon initiative? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider and services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of riders getting a coupon? A driver using the app picks up two passengers. Determine:

• The probability of both riders getting the coupon.

• The probability that only one of them will get the coupon.

• What is a confidence interval for a statistic and why is it useful? Explain what a confidence interval is, why it is useful to know the confidence interval for a statistic, and how to calculate it.

• What is the probability of finding an item on Amazon's website? Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that a specific item X is available at warehouse A (0.6) or warehouse B (0.8), calculate the probability that the item X would be found on Amazon's website.

• Is a coin fair if it comes up tails 8 times out of 10 flips? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if this coin is fair.

• What are time series models and why are they needed? Describe what time series models are and explain why they are necessary when less complicated regression models are available.

• How would you justify the complexity of building a neural network model and explain predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• How would you evaluate and deploy a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate if a decision tree is the correct model, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain how random forest generates its forest of trees. Additionally, why would you choose random forest over other algorithms like logistic regression?

• How would you explain linear regression to a child, a first-year college student, and a seasoned mathematician? Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to each audience's understanding level.

• What are the key differences between classification models and regression models? Describe the main differences between classification models and regression models.

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

Q: What does Wizeline do?

Wizeline is a global digital services company that helps mid-size to Fortune 500 companies build, scale, and deliver high-quality digital products and services. We focus on solving customer challenges through human-centered experiences, digital core modernization, and intelligence everywhere (AI/ML and data).

Q: What values define Wizeline’s culture?

At Wizeline, we value Ownership, Innovation, Community, and Diversity & Inclusion. These values are embedded within our company’s DNA and drive us to create an environment where talent and diversity thrive.

Q: What does a day in the life of a Data Engineer at Wizeline look like?

As a Data Engineer at Wizeline, your daily tasks include designing and implementing product features, creating scalable data platforms, analyzing and diagnosing data workflows, and participating in client demos and requirements elicitation. You'll work within an Agile/Scrum methodology with various stakeholders.

Q: What key skills are required for a Data Engineer role at Wizeline?

Key skills include strong programming abilities with Python, experience with Spark, Scala, and SQL optimization, solid engineering foundations, expertise in cloud-scalable Data Lake solutions, and proficiency with AWS tools like Athena, S3, and Glue. Advanced English is also necessary.

Q: Why should I consider applying to Wizeline?

Wizeline stands out due to its commitment to values such as innovation, community, and respect. We offer exceptional career growth, learning opportunities, competitive benefits, and a diverse, inclusive work environment. Recognized for our employee benefits, we also provide continuous learning through Wizeline Academy and support for global mobility.

Pave your way to success with Interview Query!

Ready to take the plunge into a world of innovation, diversity, and limitless career growth? Dive deeper into what makes Wizeline an exceptional choice for your next career move. Check out our main Wizeline Interview Guide for exclusive insights and sample interview questions tailored to help you excel in your application process.

At Interview Query, we equip you with the essential tools — knowledge, confidence, and strategic guidance — to conquer your Wizeline data engineer interview challenges and excel. Explore all our company interview guides for comprehensive preparation aids.

Good luck with your interview!