HelloFresh Data Engineer Interview Questions + Guide in 2024

HelloFresh is a leading global meal-kit delivery service aiming to revolutionize how people prepare and enjoy their meals. As an industry leader, HelloFresh has built a significant presence worldwide, delivering fresh ingredients and easy-to-follow recipes directly to millions of customers’ doors. The team is diverse, innovative, and dynamic, thriving in a fast-paced, mission-driven environment.

The Data Engineer Position at HelloFresh is integral to the company’s operations, providing the technical backbone for data-driven decision-making. In this role, you will work with various teams to design, build, and maintain data pipelines, ensuring efficient data ingestion, processing, and analysis. Your work will tackle complex data challenges, use state-of-the-art technologies, and collaborate with cross-functional teams to improve products and services.

If you want to join a fast-growing, innovative company and play a pivotal role in driving data solutions, this guide will prepare you for the process. It includes common HelloFresh data engineer interview questions and useful tips. Let’s begin!

The interview process usually depends on the role and seniority; however, you can expect the following on a HelloFresh data engineer interview:

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, you will be contacted by a recruiter from the HelloFresh Talent Acquisition Team for an initial phone screening. Key details about your experiences and skill levels will be verified during this screening. Behavioral questions may also be a part of this round.

Sometimes, the hiring manager may join the screening call to discuss the role and the company. They might also touch on surface-level technical and behavioral topics. The recruiter call generally lasts about 30 minutes.

Take-Home Test

Following the initial screening, candidates who move to the next stage will receive a take-home assignment. This test usually involves practical tasks, such as building a Spark application or creating a CSV file from JSON data. The assignment is designed to evaluate your coding and problem-solving skills.

Technical Virtual Interview

Successfully completing the take-home test will earn you an invitation to a technical screening round. This technical interview is typically conducted virtually and lasts around an hour. Expect questions on a variety of topics, including data systems, data lake design, SQL queries, and Python challenges. The interview may involve live coding exercises and system design and data architecture discussions.

Onsite Interview Rounds

If you clear the virtual technical interview, you’ll be invited to onsite interview rounds. These rounds will focus on both technical and behavioral aspects. You will likely face multiple interviewers who assess your technical capabilities, system design aptitude, and cultural fit within the team.

During these onsite rounds, you may also be asked to present any take-home assignments you completed earlier. Discussions will delve deeper into your previous project experiences, technical challenges you’ve solved, and other skills relevant to the role.

Typically, interviews at HelloFresh vary by role and team, but common data engineer interviews follow a fairly standardized process across these question topics.

1. Identify first-time and repeat purchases for each product category.

You’re analyzing a user’s purchases for a retail business. Each product belongs to a category. Your task is to identify which purchases represent the first time the user has bought a product from its own category and which purchases represent repeat purchases within the product category. The id in the purchases table represents the purchase order (rows with a lower id are earlier purchases). Your code should output a table that includes every user purchase. Additionally, the table should include a boolean column with a value of 1 if the user has previously purchased a product from its category and 0 if it’s their first time buying a product from that category. Sort the results by the time purchased, in ascending order.

2. Create 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.

3. Write a function compute_deviation to calculate the standard deviation of lists in a dictionary.

Write a function compute_deviation that takes in a list of dictionaries with a key and list of integers and returns a dictionary with the standard deviation of each list. This should be done without using the NumPy built-in functions.

4. Write a query to find the percentage of search queries with all ratings less than 3.

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating of the result from 1 to 5, where 5 is high relevance, and 1 is low relevance. Write a query to get the percentage of search queries where all of the ratings for the query results are less than a rating of 3. Please round your answer to two decimal points.

5. Develop a function plan_trip to reconstruct the path of a trip from unordered flight segments.

Consider a trip from one city to another that may contain many layovers. Given the list of flights out of order, each with a starting city and end city, write a function plan_trip to reconstruct the path of the trip so the trip tickets are in order.

6. What business health metrics would you track for an e-commerce D2C sock business?

If you are in charge of an e-commerce D2C business that sells socks, what key business health metrics would you prioritize tracking on a company dashboard?

7. How would you encode a categorical variable with thousands of distinct values?

You have a categorical variable with thousands of distinct values. Describe the method you would use to encode this variable for use in a machine learning model.

8. How would you combat overfitting when building tree-based models?

You are training a classification model using tree-based methods. Explain the techniques you would employ to prevent overfitting.

9. How would you design an ML system to predict movie scores based on review text?

As an ML engineer at Netflix, you have access to reviews of 10K movies, each containing multiple sentences and a score from 1 to 10. Describe how you would design a machine learning system to predict the movie score based on the review text.

10. What is a confidence interval for a statistic, and why is it useful?

Explain what a confidence interval is, its importance in statistics, and the method to calculate it.

Here are some tips on how you can ace your HelloFresh data engineer interview:

1. Understanding of Applied ML Concepts: Since the Global AI team works heavily on advanced ML solutions, being comfortable with machine learning principles and how they apply to data engineering projects will be beneficial.

2. Hands-On Experience with Cloud Technologies: Many job postings emphasize the importance of experience with AWS, Snowflake, Docker, Kubernetes, and other cloud technologies. Be ready to discuss your hands-on experience with these tools.

3. Behavioral Preparedness: Expect questions that evaluate your cultural fit, such as “Tell me about a time you made a mistake and how you took accountability for it.” Prepare stories that highlight your problem-solving skills, teamwork, and communication abilities.

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

According to Glassdoor, HelloFresh data engineers earn between $116K and $176K per year, with an average of $142K per year.

What technical skills and experiences are required for the Data Engineer role at HelloFresh?

You’ll need strong Python and SQL proficiency and experience working with distributed systems and cloud technologies like AWS and Snowflake. Knowledge of containerization and orchestration tools like Docker and Kubernetes is also essential. Applicants generally need a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field and at least 2+ years of data engineering experience, particularly in sectors like Fulfillment, Logistics, or Supply Chain.

What technologies will I get to work with if I join HelloFresh as a Data Engineer?

At HelloFresh, you’ll have the chance to work with state-of-the-art technologies like PySpark, Airflow, Kubernetes, and more. The role involves working with advanced data products and scalable data pipelines, helping the team to derive insights and build machine learning models from complex datasets.

What can I expect in terms of benefits if I join HelloFresh as a Data Engineer?

HelloFresh offers a competitive salary, immediate 401k company match upon participation, generous parental leave, and a PTO policy. Health plans with $0 monthly premiums are effective from the first day of employment. Employees also enjoy a 75% discount on HelloFresh subscriptions, snacks, cold brew on tap, monthly catered lunches, and company-sponsored outings.

What is the company culture like at HelloFresh?

HelloFresh boasts a diverse, high-performing, international team with a collaborative and dynamic work environment. The company is mission-driven, aiming to make cooking meals from scratch more convenient and exciting. Employees are encouraged to take ownership of their projects, collaborate across disciplines, and continuously improve existing processes.

If you’re excited about building scalable data solutions, contributing to a mission-driven company, and working with state-of-the-art technologies, HelloFresh is the place for you.

If you want more insights about the company, check out our main HelloFresh Interview Guide, where we have covered many interview questions that could be asked. Additionally, explore our interview guides for other roles, such as software engineer and data analyst, to learn more about HelloFresh’s interview process for different positions.

Good luck with your interview at HelloFresh!