BNY Mellon Data Engineer Interview Questions + Guide in 2025

BNY Mellon is a leading global financial services company that plays a pivotal role in managing and servicing financial assets for clients around the world.

In the role of a Data Engineer at BNY Mellon, you will be at the forefront of data management and engineering, contributing to the development and implementation of data architecture that supports the company's strategic goals. Key responsibilities include designing and optimizing data pipelines, implementing data governance practices, and supporting data integration for analytics and reporting. A deep understanding of SQL and familiarity with algorithms is crucial, as you will often be tasked with solving complex data challenges and ensuring data quality and accessibility for various business functions. Additionally, experience in Python will be beneficial for automating data processes and analysis.

To excel in this role, you should possess strong analytical skills, attention to detail, and the ability to work collaboratively across teams. Your role will be closely aligned with BNY Mellon’s focus on innovation, data-driven decision making, and maintaining high standards of data integrity.

This guide will help you prepare for your interview by providing insights into the key skills and knowledge areas that BNY Mellon values in a Data Engineer, enhancing your confidence and readiness for the interview process.

The interview process for a Data Engineer position at BNY Mellon is structured and thorough, designed to assess both technical skills and cultural fit. The process typically unfolds in several stages:

1. Initial Screening

The first step is an initial screening, which usually takes place over a phone call with a recruiter. This conversation focuses on your background, experience, and motivation for applying to BNY Mellon. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role.

2. Online Assessment

Following the initial screening, candidates are often required to complete an online coding assessment. This assessment typically includes a series of programming questions that test your knowledge of data structures and algorithms. The questions may vary in difficulty, but they generally align with common coding challenges found on platforms like LeetCode.

3. Technical Interviews

Candidates who perform well in the online assessment will move on to one or more technical interviews. These interviews are conducted by experienced engineers and focus on a range of topics, including database management, coding proficiency, and problem-solving skills. Expect to answer questions related to SQL, algorithms, and possibly even specific technologies relevant to the role, such as Python or big data frameworks.

4. Behavioral Interview

In addition to technical skills, BNY Mellon places a strong emphasis on cultural fit. Therefore, candidates will typically undergo a behavioral interview, where they will be asked to provide examples of past experiences that demonstrate their problem-solving abilities, teamwork, and adaptability. The STAR (Situation, Task, Action, Result) method is often encouraged for structuring responses.

5. Final Interview

The final stage usually involves a wrap-up interview with the hiring manager or a senior team member. This interview may cover both technical and behavioral aspects, allowing the interviewers to gauge your overall fit for the team and the organization. It’s also an opportunity for you to ask any remaining questions about the role or the company.

Throughout the process, candidates are encouraged to demonstrate their technical expertise, problem-solving skills, and ability to work collaboratively in a team environment.

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

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

Understand the Interview Structure

The interview process at BNY Mellon typically consists of multiple rounds, including a coding assessment followed by technical interviews and an HR round. Familiarize yourself with this structure and prepare accordingly. Expect to face questions on data structures, algorithms, and your previous projects. Knowing the flow of the interview will help you manage your time and responses effectively.

Master Data Structures and Algorithms

Given the emphasis on coding skills, particularly in data structures and algorithms, ensure you are well-versed in these areas. Practice solving problems on platforms like LeetCode, focusing on medium to hard difficulty levels. Be prepared to explain your thought process and the time and space complexity of your solutions, as interviewers often ask for this insight.

Prepare for Behavioral Questions

BNY Mellon values candidates who can articulate their experiences and how they align with the company's culture. Use the STAR (Situation, Task, Action, Result) method to structure your responses to behavioral questions. Be ready to discuss your past projects, challenges faced, and how you overcame them, as well as your understanding of diversity and teamwork.

Showcase Your Technical Knowledge

In addition to coding, be prepared to discuss your knowledge of databases, data management practices, and cloud technologies. Familiarize yourself with concepts related to data lakes, data governance, and data architecture, as these are crucial for a Data Engineer role. Highlight any relevant experience with tools like SQL, Python, and big data technologies.

Engage with the Interviewers

During the interview, engage actively with your interviewers. Ask insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you assess if BNY Mellon is the right fit for you. Remember, interviews are a two-way street.

Emphasize Your Adaptability

BNY Mellon seeks self-starters who can adapt to dynamic environments. Be prepared to discuss instances where you had to learn quickly or pivot in response to changing circumstances. Highlight your ability to work collaboratively across teams and your commitment to continuous learning.

Reflect the Company Culture

BNY Mellon prides itself on its inclusive culture and commitment to diversity. Be sure to reflect these values in your responses. Share experiences that demonstrate your ability to work in diverse teams and your understanding of the importance of inclusivity in the workplace.

Follow Up

After your interview, send 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 the company, and to mention any key points from the interview that you found particularly engaging.

By following these tips and preparing thoroughly, you can present yourself as a strong candidate for the Data Engineer role at BNY Mellon. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at BNY Mellon. The interview process will likely focus on your technical skills, particularly in data management, coding, and problem-solving, as well as your ability to communicate effectively with both technical and non-technical stakeholders. Be prepared to discuss your past projects and experiences in detail, as well as demonstrate your knowledge of data architecture and engineering principles.

Technical Skills

1. Can you explain the differences between a data lake and a data warehouse?

Understanding the distinctions between these two data storage solutions is crucial for a Data Engineer, especially in a financial services context.

How to Answer

Discuss the purpose of each system, their architecture, and the types of data they store. Highlight the use cases for each and how they can complement each other in a data strategy.

Example

“A data lake is designed to store vast amounts of raw data in its native format, allowing for flexibility and scalability. In contrast, a data warehouse is optimized for structured data and is used for reporting and analysis. While a data lake can handle unstructured data, a data warehouse provides faster query performance for business intelligence applications.”

2. Describe a time when you optimized a data pipeline. What challenges did you face?

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

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response. Focus on the specific optimizations you implemented and the impact they had on performance or efficiency.

Example

“In my previous role, I noticed that our ETL process was taking too long due to redundant data transformations. I restructured the pipeline to eliminate unnecessary steps and implemented parallel processing. As a result, we reduced the processing time by 40%, which significantly improved our reporting capabilities.”

3. What are some best practices for data governance?

Data governance is critical in financial services, and understanding best practices is essential for a Data Engineer.

How to Answer

Discuss the importance of data quality, security, and compliance. Mention specific frameworks or methodologies you have used or are familiar with.

Example

“Best practices for data governance include establishing clear data ownership, implementing data quality checks, and ensuring compliance with regulations like GDPR. I have experience using frameworks like DAMA-DMBOK to guide our governance strategy, which helped us maintain high data integrity and security standards.”

4. How do you handle schema evolution in a data pipeline?

Schema evolution is a common challenge in data engineering, especially when dealing with changing data sources.

How to Answer

Explain your approach to managing schema changes, including versioning and backward compatibility strategies.

Example

“I handle schema evolution by implementing a versioning system for our data models. When a change is necessary, I ensure that the new schema is backward compatible, allowing existing processes to continue functioning. Additionally, I maintain thorough documentation to track changes and communicate them to the team.”

5. Can you explain the CAP theorem and its implications for distributed databases?

Understanding the CAP theorem is essential for designing robust data systems.

How to Answer

Define the CAP theorem and discuss its implications for data consistency, availability, and partition tolerance in distributed systems.

Example

“The CAP theorem states that in a distributed data store, you can only achieve two of the following three guarantees: Consistency, Availability, and Partition Tolerance. This means that when designing a system, I must prioritize which two aspects are most critical for our use case. For instance, in a financial application, consistency and partition tolerance might be prioritized to ensure accurate transaction processing.”

Coding and Algorithms

1. Write a SQL query to find the second highest salary from a table.

This question tests your SQL skills, which are crucial for a Data Engineer.

How to Answer

Be prepared to write the query on the spot and explain your thought process.

Example

“Here’s how I would write the query: sql SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees); This query first finds the maximum salary and then retrieves the highest salary that is less than that value.”

2. How would you implement a data structure to efficiently store and retrieve user sessions?

This question assesses your understanding of data structures and their applications.

How to Answer

Discuss the data structure you would choose and why, considering factors like time complexity and scalability.

Example

“I would use a hash map to store user sessions, where the key is the user ID and the value is the session data. This allows for O(1) average time complexity for both storing and retrieving sessions, which is essential for performance in a high-traffic application.”

3. Explain how you would approach debugging a slow-running query.

This question evaluates your problem-solving skills and understanding of database performance.

How to Answer

Outline the steps you would take to identify and resolve performance issues in a query.

Example

“I would start by analyzing the query execution plan to identify bottlenecks. Next, I would check for missing indexes and consider rewriting the query for optimization. Additionally, I would review the underlying data model to ensure it supports efficient querying.”

4. Can you describe a situation where you had to work with a large dataset? What tools did you use?

This question assesses your experience with big data technologies.

How to Answer

Discuss the tools and technologies you used to manage and analyze large datasets, as well as any challenges you faced.

Example

“In a previous project, I worked with a dataset containing millions of records. I used Apache Spark for distributed processing, which allowed us to perform complex transformations efficiently. We also utilized AWS S3 for storage, ensuring scalability and accessibility.”

5. What is your experience with data modeling? Can you provide an example?

This question evaluates your understanding of data modeling concepts and their application.

How to Answer

Discuss your experience with different data modeling techniques and provide a specific example of a model you created.

Example

“I have experience with both relational and dimensional data modeling. For instance, I designed a star schema for a sales analytics project, which included fact tables for sales transactions and dimension tables for products and customers. This structure optimized query performance for reporting and analysis.”