SAP Data Engineer Interview Questions + Guide in 2025

SAP is a market leader in end-to-end business application software, committed to helping the world run better through innovation and collaboration.

As a Data Engineer at SAP, your role is crucial in designing, implementing, and maintaining the infrastructure necessary for data processing and model training, particularly in the context of Generative AI and Foundation Models development. You will be responsible for building data pipelines that optimize the flow and transformation of data, ensuring compliance with regulations surrounding sensitive customer information. With a focus on collaboration, you will engage with cross-functional teams and stakeholders, fostering relationships that align infrastructure across various initiatives. Additionally, your leadership skills will be vital as you mentor fellow data engineers, promoting best practices and innovative solutions within a highly dynamic team environment. A strong educational background in Computer Science or Data Engineering, along with extensive experience in data engineering techniques and tools such as Python, SQL, and frameworks like Pachyderm, will set you apart as a candidate for this role.

This guide will provide you with essential insights and targeted questions to help you prepare effectively for your interview, ensuring you can showcase your expertise and alignment with SAP's values.

The interview process for a Data Engineer at SAP is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Initial Phone Screen

The first step in the interview process is an initial phone screen, usually conducted by a recruiter. This conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to SAP. The recruiter will also discuss the role in detail and gauge your alignment with SAP's values and culture. Expect questions related to your experience with data engineering, particularly in SQL and data pipeline management.

2. Technical Assessment

Following the initial screen, candidates typically undergo a technical assessment. This may be conducted via video conferencing tools, where you will engage with a technical interviewer. The focus here is on your proficiency in data engineering concepts, including SQL, data processing, and model training. You may be asked to solve problems or discuss past projects that demonstrate your technical capabilities and understanding of data infrastructure.

3. Onsite Interviews

The onsite interview stage usually consists of multiple rounds, often ranging from three to five interviews with various team members. These interviews will cover a mix of technical and behavioral questions. You can expect to discuss your experience in infrastructure development, collaboration with cross-functional teams, and your approach to mentoring junior engineers. Each interview typically lasts around 45 minutes, allowing for in-depth discussions about your skills and experiences.

4. Final Interview

In some cases, there may be a final interview with senior leadership or a hiring manager. This stage is designed to assess your strategic thinking and alignment with SAP's long-term goals. You may be asked to present a case study or discuss how you would approach specific challenges within the team or organization.

As you prepare for your interviews, it's essential to be ready for a variety of questions that will test both your technical knowledge and your ability to work collaboratively within a team.

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

Embrace the Collaborative Culture

SAP places a strong emphasis on collaboration and teamwork. During your interview, highlight your experiences working in cross-functional teams and how you’ve successfully navigated diverse perspectives to achieve common goals. Be prepared to discuss specific examples where your collaborative efforts led to successful project outcomes. This will demonstrate that you align with SAP's values and can contribute positively to their team environment.

Prepare for Technical Proficiency

Given the technical nature of the Data Engineer role, ensure you are well-versed in SQL, Python, and data pipeline construction. Review common SQL queries and be ready to discuss your experience with frameworks like Pachyderm. You may be asked to solve technical problems or discuss your approach to data processing and model training. Practicing these skills will not only boost your confidence but also showcase your readiness for the role.

Communicate Clearly and Effectively

Exceptional communication skills are crucial for this position, especially since you will be engaging with various stakeholders. Practice articulating your thoughts clearly and concisely. When discussing your technical expertise, aim to explain complex concepts in a way that is accessible to non-technical team members. This will demonstrate your ability to bridge the gap between technical and non-technical audiences, a valuable skill in a collaborative environment.

Showcase Leadership and Mentorship

SAP values leadership and the ability to mentor others. Be prepared to share examples of how you have led projects or mentored junior team members in your previous roles. Discuss your approach to fostering a culture of learning and innovation within your team. This will illustrate your potential to contribute to SAP’s mission of developing talent and driving strategic initiatives.

Adapt to the Interview Format

Be ready for a potentially non-traditional interview format, as some candidates have experienced phone interviews instead of video calls. Ensure you have a quiet space with a reliable phone connection. If you encounter any technical issues, remain calm and professional. This adaptability will reflect positively on your ability to handle unexpected situations, a key trait for a Data Engineer working in a dynamic environment.

Align with SAP’s Purpose-Driven Approach

SAP is committed to making a positive impact on the world. Familiarize yourself with their mission and values, and think about how your personal values align with theirs. Be prepared to discuss how you can contribute to SAP’s purpose-driven initiatives, particularly in the context of AI and data engineering. This alignment will resonate well with interviewers and demonstrate your commitment to the company’s goals.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Engineer role at SAP. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at SAP. The interview will likely focus on your technical skills, experience with data processing, and ability to collaborate with cross-functional teams. Be prepared to demonstrate your knowledge of data engineering principles, SQL proficiency, and your approach to infrastructure development.

Technical Skills

1. Can you explain the process of building a data pipeline from scratch?

This question assesses your understanding of data pipeline architecture and your practical experience in building one.

How to Answer

Outline the steps involved in designing, implementing, and maintaining a data pipeline, emphasizing the tools and technologies you would use.

Example

“To build a data pipeline from scratch, I would start by identifying the data sources and the required transformations. Next, I would select appropriate tools, such as Apache Airflow for orchestration and Apache Kafka for data streaming. After implementing the pipeline, I would ensure robust monitoring and logging to maintain data quality and performance.”

2. What are the key differences between SQL and NoSQL databases?

This question evaluates your knowledge of database technologies and their appropriate use cases.

How to Answer

Discuss the fundamental differences in structure, scalability, and use cases for SQL and NoSQL databases.

Example

“SQL databases are relational and use structured query language for defining and manipulating data, making them ideal for complex queries and transactions. In contrast, NoSQL databases are non-relational and can handle unstructured data, providing greater flexibility and scalability for applications that require rapid data ingestion and retrieval.”

3. Describe a challenging data engineering problem you faced and how you solved it.

This question aims to understand your problem-solving skills and your ability to handle complex data scenarios.

How to Answer

Provide a specific example, detailing the problem, your approach to solving it, and the outcome.

Example

“I once faced a challenge with a data pipeline that was experiencing significant latency. After analyzing the bottlenecks, I discovered that the data transformation process was inefficient. I optimized the transformation logic and implemented parallel processing, which reduced the processing time by 50%.”

4. How do you ensure data quality and integrity in your data pipelines?

This question assesses your understanding of data governance and quality assurance practices.

How to Answer

Discuss the methods and tools you use to validate and monitor data quality throughout the pipeline.

Example

“I ensure data quality by implementing validation checks at various stages of the pipeline, such as schema validation and data type checks. Additionally, I use tools like Great Expectations to automate data quality testing and set up alerts for any anomalies detected during processing.”

5. What experience do you have with cloud-based data engineering tools?

This question evaluates your familiarity with cloud technologies and their application in data engineering.

How to Answer

Mention specific cloud platforms and tools you have used, along with your experience in deploying data solutions in the cloud.

Example

“I have extensive experience with AWS and Google Cloud Platform, utilizing services like AWS Glue for ETL processes and Google BigQuery for data warehousing. I have successfully migrated on-premise data solutions to the cloud, improving scalability and reducing operational costs.”

Collaboration and Leadership

1. How do you approach collaboration with cross-functional teams?

This question assesses your interpersonal skills and ability to work in a team environment.

How to Answer

Describe your communication style and how you ensure alignment with different stakeholders.

Example

“I prioritize open communication and regular check-ins with cross-functional teams. I use collaborative tools like Slack and JIRA to keep everyone updated on project progress and encourage feedback to ensure that all perspectives are considered in decision-making.”

2. Can you provide an example of how you mentored a junior data engineer?

This question evaluates your leadership skills and commitment to team development.

How to Answer

Share a specific instance where you provided guidance and support to a junior team member.

Example

“I mentored a junior data engineer by pairing with them on a project to build a data pipeline. I provided them with resources and best practices, and we held regular review sessions to discuss their progress. This not only helped them grow their skills but also fostered a collaborative team environment.”

3. How do you handle conflicts within a team?

This question assesses your conflict resolution skills and ability to maintain a positive team dynamic.

How to Answer

Discuss your approach to addressing conflicts and ensuring a constructive resolution.

Example

“When conflicts arise, I believe in addressing them directly and promptly. I facilitate a discussion where each party can express their concerns, and I work towards finding a compromise that aligns with our project goals. This approach has helped maintain a collaborative atmosphere within the team.”

4. Describe a time when you had to influence a decision made by stakeholders.

This question evaluates your ability to advocate for your ideas and influence others.

How to Answer

Provide an example of a situation where you successfully influenced a decision, detailing your approach and the outcome.

Example

“I once proposed a new data processing framework that would improve efficiency. I gathered data on the potential benefits and presented it to stakeholders, highlighting the long-term cost savings. After addressing their concerns, they agreed to implement the framework, which resulted in a 30% reduction in processing time.”

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

This question assesses your commitment to continuous learning and professional development.

How to Answer

Discuss the resources and methods you use to keep your skills current in the rapidly evolving field of data engineering.

Example

“I stay updated by following industry blogs, participating in webinars, and attending conferences. I also engage with online communities and forums where data engineers share insights and best practices, which helps me stay informed about the latest tools and technologies.”