Nomura Data Engineer Interview Questions + Guide in 2025

Nomura is a leading financial services group that provides a broad range of investment banking, asset management, and retail services to clients worldwide.

As a Data Engineer at Nomura, you will play a pivotal role in designing, building, and maintaining scalable data pipelines and architectures that support data-driven decision-making across the organization. You will be responsible for transforming raw data into a format that is accessible and usable for analysis, working closely with data scientists and analysts to understand their data needs. Key responsibilities include optimizing data flow, ensuring data quality, and implementing data governance practices.

To excel in this role, you will need strong technical skills, particularly in SQL and Python, and a solid understanding of algorithms and data structures. Familiarity with data warehousing solutions and big data technologies is advantageous. Additionally, you should possess a keen analytical mindset and problem-solving abilities, as well as excellent communication skills to collaborate effectively with cross-functional teams. A proactive approach and adaptability to evolving technologies will also help you thrive in Nomura's dynamic environment.

This guide will help you prepare effectively for your interview by providing insights into the role, key skills to focus on, and the types of questions you may encounter, ensuring you present yourself as a strong candidate for the Data Engineer position.

The interview process for a Data Engineer at Nomura is structured and thorough, designed to assess both technical skills and cultural fit within the organization. The process typically unfolds in several distinct stages:

1. Initial Screening

The first step is an initial screening, which usually takes place over a phone call with a recruiter or hiring manager. This conversation is generally focused on your background, experience, and motivation for applying to Nomura. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may involve an online coding test or a technical interview where you will be asked to solve problems related to data structures, algorithms, and SQL. Expect questions that assess your proficiency in programming languages relevant to the role, such as Python, as well as your understanding of database management and data manipulation techniques.

3. Technical Interviews

Candidates who pass the technical assessment will move on to multiple technical interviews, often conducted by senior engineers or team leads. These interviews can be quite rigorous, lasting around an hour each, and may include a mix of coding challenges, system design questions, and discussions about past projects. You may be asked to explain your approach to problem-solving and how you have applied your technical skills in real-world scenarios.

4. Behavioral Interview

In addition to technical evaluations, there is typically a behavioral interview component. This stage focuses on assessing your interpersonal skills, teamwork, and alignment with Nomura's values. Expect questions that explore how you handle challenges, work in teams, and contribute to a collaborative environment.

5. Final Interview

The final stage often includes a wrap-up interview with HR or higher management. This session may cover your long-term career aspirations, fit within the company culture, and any remaining questions you have about the role or the organization. It’s also an opportunity for you to demonstrate your enthusiasm for the position and the company.

Throughout the process, candidates should be prepared for a variety of questions that test both their technical knowledge and their ability to communicate effectively.

Next, let’s delve into the specific interview questions that candidates have encountered during their interviews at Nomura.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Nomura. The interview process will likely assess your technical skills, problem-solving abilities, and understanding of data engineering concepts. Be prepared to discuss your past projects, demonstrate your knowledge of SQL, algorithms, and programming languages, and showcase your analytical skills.

Technical Skills

1. What is your experience with SQL? Can you explain the difference between INNER JOIN and LEFT JOIN?

Understanding SQL is crucial for a Data Engineer role, and interviewers will want to gauge your proficiency with database queries.

How to Answer

Discuss your experience with SQL, including specific projects where you utilized it. Clearly explain the differences between INNER JOIN and LEFT JOIN, providing examples of when to use each.

Example

“I have extensive experience with SQL, particularly in data extraction and transformation. An INNER JOIN returns only the rows with matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. For instance, in a project where I merged customer data with transaction records, I used LEFT JOIN to ensure all customers were included, even those without transactions.”

2. Can you explain normalization and its importance in database design?

Normalization is a key concept in database management, and interviewers will want to see if you understand its significance.

How to Answer

Define normalization and discuss its purpose in reducing data redundancy and improving data integrity. Mention the different normal forms briefly.

Example

“Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller ones and defining relationships between them. For example, I applied normalization in a project to separate customer information from order details, which helped maintain data consistency and made updates easier.”

3. Describe a challenging data pipeline you built. What technologies did you use?

This question assesses your practical experience in building data pipelines.

How to Answer

Detail the project, the challenges faced, and the technologies you used. Highlight your problem-solving skills and the impact of the pipeline.

Example

“I built a data pipeline for processing real-time financial transactions using Apache Kafka and Spark. The challenge was to ensure low latency while handling high volumes of data. I implemented a micro-batch processing approach, which allowed us to achieve near real-time processing and significantly improved our reporting capabilities.”

4. How do you ensure data quality in your projects?

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

How to Answer

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

Example

“To ensure data quality, I implement validation checks at various stages of the data pipeline. I use tools like Apache NiFi for data ingestion, which allows me to set up rules for data validation. Additionally, I perform regular audits and use automated testing frameworks to catch any discrepancies early in the process.”

5. What is your experience with ETL processes? Can you describe a specific ETL project?

ETL (Extract, Transform, Load) processes are fundamental in data engineering, and interviewers will want to assess your familiarity with them.

How to Answer

Provide an overview of your experience with ETL processes and describe a specific project, including the tools and technologies used.

Example

“I have worked extensively with ETL processes using tools like Talend and Apache Airflow. In one project, I developed an ETL pipeline to aggregate sales data from multiple sources into a centralized data warehouse. I designed the transformation logic to clean and enrich the data, which improved our analytics capabilities and provided more accurate insights for decision-making.”

Algorithms and Data Structures

1. Can you explain the concept of a hash table and its use cases?

Understanding data structures is essential for a Data Engineer, and hash tables are a common topic.

How to Answer

Define a hash table and discuss its advantages, as well as scenarios where it is particularly useful.

Example

“A hash table is a data structure that maps keys to values for efficient data retrieval. It uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. Hash tables are particularly useful for implementing associative arrays and caching, where quick lookups are essential.”

2. How would you find the first non-repeating character in a string?

This question tests your problem-solving skills and understanding of algorithms.

How to Answer

Outline your approach to solving the problem, including any algorithms or data structures you would use.

Example

“I would use a hash map to count the occurrences of each character in the string. Then, I would iterate through the string a second time to find the first character with a count of one. This approach ensures a time complexity of O(n), which is efficient for this problem.”

3. Describe a situation where you had to optimize a slow-running query. What steps did you take?

This question assesses your ability to troubleshoot and optimize performance.

How to Answer

Discuss the specific query, the performance issues encountered, and the steps you took to optimize it.

Example

“I encountered a slow-running query that was aggregating sales data over a large dataset. I analyzed the execution plan and identified missing indexes as a key issue. After adding the appropriate indexes and rewriting the query to reduce complexity, I improved the execution time by over 50%.”

4. What is the difference between a stack and a queue?

This question tests your understanding of basic data structures.

How to Answer

Define both data structures and explain their differences, including use cases for each.

Example

“A stack is a data structure that follows the Last In First Out (LIFO) principle, meaning the last element added is the first to be removed. A queue, on the other hand, follows the First In First Out (FIFO) principle. Stacks are often used in function call management, while queues are used in scheduling tasks or managing requests in a system.”

5. Can you explain the concept of Big O notation and its importance?

Understanding algorithm efficiency is crucial for a Data Engineer, and Big O notation is a key concept.

How to Answer

Define Big O notation and discuss its significance in evaluating algorithm performance.

Example

“Big O notation is a mathematical representation used to describe the performance or complexity of an algorithm in terms of time or space as the input size grows. It helps in comparing the efficiency of different algorithms and is crucial for optimizing code, especially when dealing with large datasets.”