Zalando SE Data Engineer Interview Questions + Guide in 2025

Zalando SE is a leading online platform for fashion that connects customers, brands, and partners across Europe, with a commitment to delivering inclusive and innovative solutions in e-commerce.

As a Data Engineer in the Connected Network department, your primary responsibility will be to design, implement, and maintain robust data pipelines and systems that support Zalando’s logistics solutions. You will collaborate closely with Software Engineers, Applied Scientists, and Data Analysts to define the data strategy that drives the analytics platform and data science initiatives, fundamentally transforming how partners engage with logistics services.

Key responsibilities include building and optimizing ETL pipelines, ensuring data quality, and working with distributed data processing frameworks such as Spark SQL and PySpark. You will also be expected to demonstrate proficiency in Python, SQL, and cloud-based data solutions, particularly AWS. A strong team player mindset and excellent communication skills are essential as you will be mentoring colleagues and promoting clean data engineering practices within your team.

This guide is designed to help you prepare thoroughly for your interview by providing insights into the role and the skills that are highly valued by Zalando. It will equip you with the knowledge needed to showcase your technical expertise and alignment with the company's values.

The interview process for a Data Engineer at Zalando is structured to assess both technical skills and cultural fit within the organization. Here’s what you can expect:

1. Initial Screening

The process begins with an initial screening, typically conducted by a recruiter over a phone call. This conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Zalando. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you understand the expectations and responsibilities.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment. This may take place via a video call and will involve a data engineering-focused interview with a senior data engineer or technical lead. Expect to discuss your experience with SQL, Python, and ETL processes, as well as your familiarity with distributed data processing frameworks like Spark SQL and PySpark. You may also be asked to solve a coding challenge or work through a case study that demonstrates your problem-solving abilities and technical expertise.

3. Onsite Interviews

The onsite interview consists of multiple rounds, typically ranging from three to five interviews with various team members, including data engineers, applied scientists, and product managers. Each interview lasts approximately 45 minutes and covers a mix of technical and behavioral questions. You will be evaluated on your ability to design and implement data pipelines, your understanding of data modeling and data quality testing, and your experience with cloud-based data solutions, particularly on AWS. Additionally, expect discussions around your teamwork and communication skills, as collaboration is key in Zalando's multi-functional setup.

4. Final Interview

The final stage of the interview process may include a conversation with a senior leader or manager within the Connected Network department. This interview will focus on your alignment with Zalando's values and culture, as well as your long-term career aspirations. It’s an opportunity for you to ask questions about the team dynamics, ongoing projects, and the company’s vision for the future.

As you prepare for your interviews, consider the specific skills and experiences that will be relevant to the questions you may encounter. Next, we will delve into the types of questions that candidates have faced during the interview process.

Zalando Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Zalando Data Engineer interview. The interview will assess your technical skills in data engineering, including your proficiency in SQL, Python, and distributed data processing, as well as your ability to work collaboratively in a team environment. Be prepared to discuss your experience with ETL processes, data modeling, and cloud solutions.

Technical Skills

1. Can you explain the ETL process and its importance in data engineering?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is the backbone of data integration and management.

How to Answer

Discuss the steps involved in ETL, emphasizing how each step contributes to data quality and accessibility for analytics.

Example

“The ETL process involves extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse. This process is vital as it ensures that data is clean, consistent, and readily available for analysis, enabling informed decision-making.”

2. How do you ensure data quality in your pipelines?

Data quality is essential for reliable analytics and decision-making.

How to Answer

Mention specific techniques you use to validate and monitor data quality, such as data profiling, validation rules, and automated testing.

Example

“I implement data validation checks at various stages of the ETL process, such as ensuring data types match expected formats and checking for null values. Additionally, I use automated tests to monitor data quality continuously, allowing for quick identification and resolution of issues.”

3. Describe your experience with SQL and how you use it in data engineering.

SQL is a fundamental skill for Data Engineers, as it is used for querying and managing data.

How to Answer

Highlight your proficiency in SQL, including specific functions or queries you frequently use, and how they contribute to your data engineering tasks.

Example

“I have extensive experience with SQL, particularly in writing complex queries to extract and manipulate data. I often use window functions and joins to analyze data across multiple tables, which is essential for generating insights and reports.”

4. What is your experience with distributed data processing frameworks like Spark?

Familiarity with distributed processing is important for handling large datasets efficiently.

How to Answer

Discuss your experience with Spark or similar frameworks, including specific projects where you utilized them.

Example

“I have worked with Apache Spark for distributed data processing, particularly using PySpark for data transformations. In a recent project, I built a data pipeline that processed terabytes of data in parallel, significantly reducing processing time compared to traditional methods.”

5. Can you explain the concept of data modeling and its significance?

Data modeling is a critical aspect of data engineering that impacts how data is structured and accessed.

How to Answer

Define data modeling and discuss its importance in creating efficient databases and ensuring data integrity.

Example

“Data modeling involves designing the structure of a database, including defining entities, attributes, and relationships. It is significant because a well-structured model enhances data retrieval efficiency and ensures data integrity, which is crucial for analytics.”

Collaboration and Problem-Solving

1. Describe a challenging data engineering problem you faced and how you resolved it.

Problem-solving skills are essential for Data Engineers, especially when dealing with complex data issues.

How to Answer

Provide a specific example of a challenge, the steps you took to address it, and the outcome.

Example

“I encountered a challenge with data inconsistency across multiple sources. I conducted a thorough analysis to identify discrepancies and implemented a data cleansing process that standardized the data formats. This not only resolved the issue but also improved the overall data quality for future analyses.”

2. How do you prioritize tasks when working on multiple data projects?

Time management and prioritization are key skills for managing multiple responsibilities.

How to Answer

Discuss your approach to prioritizing tasks, including any frameworks or tools you use.

Example

“I prioritize tasks based on project deadlines and the impact on business objectives. I use project management tools to track progress and ensure that I allocate time effectively to high-priority tasks while remaining flexible to accommodate urgent requests.”

3. How do you handle feedback from team members or stakeholders?

Collaboration and communication are vital in a team environment.

How to Answer

Explain your approach to receiving and implementing feedback, emphasizing your openness to improvement.

Example

“I view feedback as an opportunity for growth. I actively seek input from team members and stakeholders, and I take the time to understand their perspectives. When I receive feedback, I assess it critically and implement changes where appropriate to enhance my work and the overall project.”

4. Can you give an example of how you have mentored a colleague in data engineering?

Mentorship is important for fostering a collaborative team culture.

How to Answer

Share a specific instance where you provided guidance or support to a colleague.

Example

“I mentored a junior data engineer who was new to ETL processes. I organized a series of training sessions where I walked them through our data pipeline architecture and best practices for data quality. This not only helped them gain confidence but also improved our team’s overall efficiency.”

5. How do you stay updated with the latest trends and technologies in data engineering?

Continuous learning is essential in the rapidly evolving field of data engineering.

How to Answer

Discuss the resources you use to stay informed, such as online courses, webinars, or industry publications.

Example

“I regularly follow industry blogs, attend webinars, and participate in online courses to stay updated on the latest trends and technologies in data engineering. I also engage with the data engineering community on platforms like LinkedIn and GitHub to share knowledge and learn from others.”