Freewheel Data Engineer Interview Questions + Guide in 2025

Freewheel is a leading platform that specializes in advanced advertising solutions for television and digital media, helping companies optimize their advertising strategies and maximize revenue.

As a Data Engineer at Freewheel, you will play a crucial role in designing, building, and maintaining the data infrastructure that supports the company's advertising solutions. Your key responsibilities will include developing data pipelines, ensuring data quality, and collaborating with data scientists and analysts to facilitate data accessibility and usability. A strong grasp of big data technologies, cloud platforms (particularly AWS), and familiarity with SQL and NoSQL databases will be vital for success in this role. Additionally, a solid understanding of data processing frameworks like Hadoop and experience with data warehousing concepts will set you apart.

Freewheel values teamwork and technical expertise, so demonstrating strong communication skills alongside your technical capabilities will be essential. This guide will help you prepare for the interview by equipping you with insights into the role's expectations and the types of questions you may encounter, giving you a competitive edge.

The interview process for a Data Engineer position at Freewheel is structured to assess both technical expertise and cultural fit within the company. The process typically unfolds in several key stages:

1. Initial Phone Screen

The first step is a phone interview with a recruiter, lasting about 30 minutes. This conversation focuses on your background, skills, and motivations for applying to Freewheel. The recruiter will also gauge your understanding of the role and the company culture, ensuring that you align with Freewheel's values.

2. Technical Assessment

Following the initial screen, candidates are usually required to complete a technical assessment, often conducted through platforms like HackerRank. This assessment typically includes a mix of coding challenges, such as one easy and one medium difficulty question, along with a SQL-related task. The goal is to evaluate your problem-solving abilities and coding proficiency in a practical context.

3. Take-Home Assignment

Candidates may be asked to complete a take-home assignment that involves preparing a presentation on a relevant technical topic. This step allows you to showcase your knowledge and communication skills, as well as your ability to convey complex information clearly and effectively.

4. Onsite Interview

The onsite interview is a comprehensive evaluation that usually lasts around three hours. It begins with a presentation of your take-home assignment to a group of interviewers, followed by a series of one-on-one meetings with various leads and managers. During these sessions, you will be assessed on your technical skills, experience, and interpersonal abilities. Expect to discuss your past projects, particularly those related to big data technologies, and be prepared to answer in-depth questions about your technical expertise, including cloud services like AWS and data processing frameworks such as Hadoop.

As you prepare for your interview, consider the types of questions that may arise during this process.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

Familiarize yourself with the interview process at Freewheel, which typically includes an initial phone screen, a take-home assignment, and an onsite interview. The take-home assignment often requires you to prepare a presentation on a technical topic, so choose a subject you are passionate about and can discuss confidently. During the onsite portion, you will present to a group and meet with various leads and managers. This is your opportunity to showcase not only your technical skills but also your ability to communicate and collaborate effectively.

Prepare for Technical Assessments

Expect to encounter coding challenges, including a mix of easy and medium-level problems, as well as SQL questions. Brush up on your coding skills using platforms like LeetCode or HackerRank, focusing on data structures, algorithms, and SQL queries. Make sure you can explain your thought process clearly while solving problems, as this will demonstrate your analytical skills and approach to problem-solving.

Highlight Your Big Data Experience

Given the emphasis on Big Data technologies, be prepared to discuss your experience with tools like AWS, Hadoop, and HDFS. Understand the differences between these technologies and be ready to articulate your opinions on their advantages and disadvantages. This knowledge will not only show your technical expertise but also your ability to make informed decisions in a data engineering context.

Revise Your Past Projects

Reflect on your previous projects and be ready to discuss them in detail. Choose a project that you are particularly proud of and can explain the challenges you faced, the solutions you implemented, and the impact it had. This will help you convey your hands-on experience and problem-solving capabilities, which are crucial for a Data Engineer role.

Emphasize Soft Skills

While technical skills are essential, Freewheel also values interpersonal skills. Be prepared to demonstrate your ability to work in a team, communicate effectively, and adapt to different situations. During your interviews, showcase your collaborative spirit and how you can contribute positively to the team culture.

Be Authentic and Engaged

Throughout the interview process, be yourself. Authenticity resonates well with interviewers and helps them gauge if you would be a good cultural fit for the company. Show enthusiasm for the role and the company, and engage with your interviewers by asking insightful questions about their work and the team dynamics.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at Freewheel. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Freewheel. The interview process will likely assess your technical skills, problem-solving abilities, and understanding of data engineering concepts. Be prepared to discuss your experience with big data technologies, cloud platforms, and data pipeline architecture.

Technical Skills

1. Can you explain the differences between HDFS and AWS S3? Which one would you prefer for a specific use case and why?

Understanding the strengths and weaknesses of different storage solutions is crucial for a Data Engineer.

How to Answer

Discuss the characteristics of both HDFS and AWS S3, including scalability, cost, and performance. Provide a specific use case to illustrate your preference.

Example

“HDFS is great for high-throughput access to large datasets, especially in a Hadoop ecosystem, while AWS S3 offers more flexibility and scalability for cloud-based applications. For a project requiring rapid scaling and cost-effectiveness, I would prefer AWS S3 due to its pay-as-you-go model and ease of integration with other AWS services.”

2. Describe a data pipeline you have built. What challenges did you face, and how did you overcome them?

This question assesses your practical experience and problem-solving skills in data engineering.

How to Answer

Outline the architecture of the pipeline, the technologies used, and the specific challenges encountered. Highlight your problem-solving approach.

Example

“I built a data pipeline using Apache Kafka and Spark to process real-time data from various sources. One challenge was ensuring data consistency during high load. I implemented a checkpointing mechanism in Spark to handle failures gracefully, which significantly improved the reliability of the pipeline.”

Coding and Algorithms

3. Can you solve a LeetCode-style problem involving data structures?

Expect to demonstrate your coding skills through algorithmic challenges.

How to Answer

Walk through your thought process as you solve the problem, explaining your approach and the data structures you choose.

Example

“Given a list of integers, I would use a hash map to track the frequency of each number. This allows me to find duplicates in O(n) time complexity. I would iterate through the list, updating the hash map, and then return the numbers that appear more than once.”

4. Write a SQL query to find the top 10 customers by total purchase amount.

SQL proficiency is essential for data manipulation and analysis.

How to Answer

Clearly explain your SQL logic and the structure of your query, focusing on the use of aggregate functions and ordering.

Example

“I would use the following SQL query: SELECT customer_id, SUM(purchase_amount) AS total_amount FROM purchases GROUP BY customer_id ORDER BY total_amount DESC LIMIT 10; This query aggregates the purchase amounts by customer and retrieves the top 10 based on total spending.”

Big Data Technologies

5. What is your experience with Hadoop and its ecosystem? Can you explain the role of YARN?

This question evaluates your familiarity with big data frameworks.

How to Answer

Discuss your hands-on experience with Hadoop components and explain YARN's function in resource management.

Example

“I have worked extensively with Hadoop, particularly with HDFS for storage and MapReduce for processing. YARN acts as the resource manager, allowing multiple applications to share resources efficiently. It separates resource management from data processing, which enhances cluster utilization.”

6. How do you ensure data quality in your data engineering processes?

Data quality is critical in data engineering, and interviewers want to know your strategies.

How to Answer

Discuss the methods you use to validate and clean data, as well as any tools or frameworks you employ.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Apache NiFi for data ingestion. I also create automated tests to check for anomalies and inconsistencies, ensuring that only high-quality data enters the system.”