Rakuten Data Engineer Interview Questions + Guide in 2025

Rakuten is a leading global technology company known for its robust e-commerce platforms and innovative financial services, serving millions of customers worldwide.

As a Data Engineer at Rakuten, you will play a critical role in building and optimizing data pipelines that facilitate the processing and analysis of large datasets to support various business initiatives. Key responsibilities include designing and implementing data storage solutions, developing ETL processes, collaborating with data scientists and analysts to understand data requirements, and ensuring data integrity and quality. A strong proficiency in programming languages such as Python and Java, as well as experience with big data technologies like Hadoop and Spark, is essential for this role. You should also be well-versed in SQL for querying databases and possess a solid understanding of cloud services, particularly AWS or GCP. Being detail-oriented and having a strong problem-solving mindset will make you an excellent fit for the data-driven culture at Rakuten.

This guide will help you prepare for your interview by contextualizing the expectations and requirements of the Data Engineer role, allowing you to align your skills and experiences with Rakuten's business objectives and values.

The interview process for a Data Engineer position at Rakuten is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages designed to evaluate a candidate's experience, problem-solving abilities, and alignment with Rakuten's values.

1. Application and Initial Screening

Candidates begin by submitting their application through Rakuten's website or a job portal. The HR team reviews applications to ensure candidates meet the basic qualifications for the role. This initial screening may involve a brief phone call to discuss the candidate's background and interest in the position.

2. Technical Assessment

If the candidate passes the initial screening, they are invited to complete a technical assessment. This assessment often includes coding challenges that can be done on platforms like Codility or HackerRank, focusing on data structures, algorithms, and relevant programming languages such as Python and Java. Candidates may encounter questions that require them to demonstrate their understanding of SQL and big data tools.

3. Phone Interview

Following the technical assessment, candidates typically participate in a phone interview with a hiring manager or a team member. This interview delves deeper into the candidate's technical skills, experience with data engineering tools, and understanding of cloud environments. Candidates should be prepared to discuss their previous projects and how they have applied their technical knowledge in real-world scenarios.

4. On-Site or Virtual Interview

Candidates who perform well in the phone interview may be invited to an on-site or virtual interview. This stage usually consists of multiple rounds, including technical interviews with engineers and behavioral interviews with managers. Technical interviews may involve live coding exercises, system design questions, and discussions about data modeling and architecture. Behavioral interviews focus on the candidate's work style, problem-solving approach, and how they align with Rakuten's culture.

5. Final Decision

After completing the interview rounds, the Rakuten team evaluates the candidate's overall fit for the role, considering both technical skills and cultural alignment. Candidates can expect to receive feedback and a decision within a few days to a couple of weeks following the final interview.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during this process.

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

Understand the Role Expectations

Before your interview, take the time to clarify the expectations for the Data Engineer role at Rakuten. Given the dynamic nature of software engineering, expectations can evolve significantly over time. Prepare to ask your interviewers how the role's responsibilities may change in the first 30, 60, or 90 days. This shows your proactive approach and genuine interest in aligning with the team's goals.

Master the Technical Skills

Focus on honing your skills in Python, SQL, and big data technologies such as Spark and Hadoop. Given the emphasis on production-level coding, practice writing clean, efficient, and well-documented code. Familiarize yourself with common machine learning frameworks like TensorFlow and PyTorch, as well as cloud platforms like AWS and GCP. Be prepared to discuss your experience with CI/CD practices and how you have implemented them in past projects.

Prepare for Behavioral Questions

Rakuten places a strong emphasis on cultural fit, so be ready to discuss your previous experiences and how they align with the company's values. Expect questions about teamwork, problem-solving, and how you handle challenges. Reflect on your past projects and be prepared to share specific examples that demonstrate your skills and adaptability.

Showcase Your Problem-Solving Skills

During technical interviews, you may encounter coding challenges that require you to think critically and solve problems on the spot. Practice common data structures and algorithms, and be ready to explain your thought process as you work through problems. Interviewers appreciate candidates who can articulate their reasoning and approach to problem-solving.

Engage with the Interviewers

Rakuten's interview process often includes multiple rounds with different team members. Use this opportunity to engage with your interviewers by asking insightful questions about their work, the team dynamics, and the projects you would be involved in. This not only demonstrates your interest in the role but also helps you assess if the company culture aligns with your values.

Be Ready for a Fast-Paced Process

The interview process at Rakuten can be quick and efficient, often involving multiple rounds in a short timeframe. Stay organized and responsive throughout the process. If you receive a coding test or technical assessment, manage your time effectively to ensure you can showcase your skills without feeling rushed.

Follow Up Professionally

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the role and briefly highlight how your skills align with the team's needs. A thoughtful follow-up can leave a positive impression and keep you top of mind for the hiring team.

By preparing thoroughly and approaching the interview with confidence and curiosity, you can position yourself as a strong candidate for the Data Engineer role at Rakuten. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Rakuten. The interview process will likely focus on your technical skills, experience with data engineering tools, and your ability to work within a team. Be prepared to discuss your past projects, coding practices, and how you approach problem-solving in data engineering contexts.

Technical Skills

1. Can you explain the differences between SQL and NoSQL databases?

Understanding the strengths and weaknesses of different database types is crucial for a Data Engineer.

How to Answer

Discuss the use cases for each type of database, highlighting their scalability, flexibility, and performance characteristics.

Example

"SQL databases are structured and ideal for complex queries, while NoSQL databases offer flexibility and scalability for unstructured data. For instance, I would use SQL for transactional data where relationships are important, and NoSQL for large-scale applications requiring high availability and quick access to varied data types."

2. Describe your experience with data modeling and architecture.

This question assesses your ability to design efficient data systems.

How to Answer

Provide examples of data models you've created and the rationale behind your design choices.

Example

"In my previous role, I designed a star schema for a data warehouse that improved query performance by 30%. I focused on normalization to reduce redundancy while ensuring that the model supported complex analytical queries."

3. How do you ensure data accuracy and integrity in your work?

Data integrity is critical in data engineering roles.

How to Answer

Discuss the methods you use to validate data and maintain its quality throughout the pipeline.

Example

"I implement data validation checks at various stages of the ETL process, using automated tests to catch anomalies. Additionally, I regularly audit data against source systems to ensure consistency."

4. Can you walk us through a particularly challenging data engineering problem you've solved?

This question evaluates your problem-solving skills and technical expertise.

How to Answer

Choose a specific example that demonstrates your analytical skills and technical knowledge.

Example

"I once faced a challenge with a data pipeline that was failing due to inconsistent data formats. I implemented a preprocessing step that standardized the data before it entered the pipeline, which reduced errors by 40% and improved overall processing time."

5. What big data tools have you worked with, and how did you use them?

This question assesses your familiarity with industry-standard tools.

How to Answer

Mention specific tools and describe how you applied them in your projects.

Example

"I have extensive experience with Apache Spark for processing large datasets. In a recent project, I used Spark to analyze user behavior data, which allowed us to derive insights that improved our recommendation engine's accuracy."

Programming and Coding

1. What is your experience with Python and its libraries for data engineering?

Python is a key language in data engineering, and familiarity with its libraries is essential.

How to Answer

Discuss your proficiency with Python and any relevant libraries you have used.

Example

"I have used Python extensively for data manipulation and analysis, particularly with libraries like Pandas and NumPy. For instance, I utilized Pandas to clean and transform a large dataset, which was crucial for our machine learning model training."

2. How do you approach writing production-level code?

This question evaluates your coding standards and practices.

How to Answer

Explain your coding practices, including documentation, testing, and code reviews.

Example

"I adhere to best practices by writing clean, modular code with comprehensive documentation. I also implement unit tests to ensure functionality and participate in code reviews to maintain high standards across the team."

3. Can you explain the concept of CI/CD and its importance in data engineering?

Understanding CI/CD is vital for modern data engineering practices.

How to Answer

Discuss the principles of CI/CD and how they apply to data engineering workflows.

Example

"CI/CD allows for automated testing and deployment of data pipelines, which minimizes downtime and ensures that changes are integrated smoothly. In my last project, implementing CI/CD reduced our deployment time by 50%."

4. Describe your experience with orchestration tools like Airflow or Kubeflow.

Orchestration tools are essential for managing complex data workflows.

How to Answer

Share your experience with these tools and how you have used them to manage data pipelines.

Example

"I have used Apache Airflow to schedule and monitor our ETL processes. By creating directed acyclic graphs (DAGs), I was able to automate data workflows, which improved our data processing efficiency significantly."

5. What are some common performance tuning techniques you have applied?

Performance tuning is crucial for optimizing data systems.

How to Answer

Discuss specific techniques you have used to enhance performance.

Example

"I often use indexing and partitioning in databases to improve query performance. In one instance, I partitioned a large table by date, which reduced query times from minutes to seconds."

Behavioral Questions

1. Why do you want to work at Rakuten?

This question assesses your motivation and fit for the company culture.

How to Answer

Express your interest in Rakuten's mission and how it aligns with your career goals.

Example

"I admire Rakuten's commitment to innovation in e-commerce and fintech. I believe my skills in data engineering can contribute to enhancing user experiences and driving business growth."

2. How do you handle tight deadlines and pressure?

This question evaluates your ability to work under stress.

How to Answer

Provide an example of a time you successfully managed a tight deadline.

Example

"During a critical project, we faced a tight deadline due to unexpected changes. I prioritized tasks, communicated effectively with my team, and we managed to deliver the project on time without compromising quality."

3. Can you describe a time when you had to work collaboratively with a team?

Collaboration is key in data engineering roles.

How to Answer

Share an example that highlights your teamwork skills.

Example

"I worked on a cross-functional team to develop a new data pipeline. By holding regular meetings and using collaborative tools, we ensured everyone was aligned, which led to a successful project launch."

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

This question assesses your commitment to continuous learning.

How to Answer

Discuss the resources you use to keep your skills current.

Example

"I regularly read industry blogs, participate in webinars, and attend conferences. I also engage with online communities to share knowledge and learn from peers."

5. What do you consider your greatest strength as a Data Engineer?

This question allows you to highlight your key skills.

How to Answer

Identify a strength that is relevant to the role and provide an example.

Example

"My greatest strength is my analytical mindset. I excel at breaking down complex problems and finding efficient solutions, which has been instrumental in optimizing our data workflows."