Nerdwallet Data Engineer Interview Questions + Guide in 2025

Nerdwallet is a financial technology company that provides consumers with tools to make informed financial decisions and manage their personal finances effectively.

As a Data Engineer at Nerdwallet, you will play a critical role in shaping the company's data landscape. Your primary responsibilities will involve designing, developing, and maintaining robust data pipelines and data models that provide valuable insights for the business. You will ensure data integrity by implementing proactive monitoring and troubleshooting data issues, all while adhering to the highest data quality standards. Collaboration is key; you will work closely with other Data Engineers, Product Managers, and Analytics teams to understand and address their data needs.

In this role, you will leverage your expertise in SQL and Python to build and operate data systems, focusing on efficiency, scalability, and performance. Your experience with modern data tools such as AWS, Snowflake, DBT, and Airflow will be crucial in integrating new data sources and enhancing the company's central data repository. You will also be responsible for analyzing complex technical issues and proposing innovative solutions that drive continuous improvement in data engineering practices.

The ideal candidate will have a strong understanding of relational databases, excellent communication skills, and a proactive approach to problem-solving. At Nerdwallet, we value a culture of trust, inclusivity, and collaboration, so being a team player and demonstrating accountability in your work will be essential to your success.

This guide will help you prepare for a job interview by providing insights into the core responsibilities and skills required for the Data Engineer role at Nerdwallet, ensuring you can showcase your qualifications effectively.

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

1. Initial Recruiter Call

The process begins with a phone interview conducted by a recruiter. This initial call usually lasts around 30 minutes and serves as an opportunity for the recruiter to gauge your interest in the role, discuss your background, and evaluate your fit for Nerdwallet's culture. Expect to discuss your experience with data engineering concepts, tools, and methodologies.

2. Technical Assessment

Following the recruiter call, candidates typically undergo a technical assessment. This may be conducted via a video call and often includes hands-on coding challenges. You can expect questions focused on SQL, including advanced functions like RANK() and ROW_NUMBER(), as well as practical problems that require you to demonstrate your ability to manipulate and analyze data. Be prepared to solve algorithmic challenges and discuss your approach to data modeling and pipeline design.

3. Technical Interview

The next step usually involves one or more technical interviews with members of the data engineering team. These interviews delve deeper into your technical expertise, particularly in SQL, Python, and data modeling. You may be asked to explain your experience with data pipeline development, data quality standards, and the tools you have used, such as AWS, Snowflake, and DBT. Expect to discuss your problem-solving strategies and how you approach data integrity and reliability.

4. Behavioral Interview

In addition to technical skills, Nerdwallet places a strong emphasis on cultural fit and collaboration. A behavioral interview will likely be part of the process, where you will be asked to share examples of past experiences that demonstrate your communication skills, teamwork, and ability to adapt to new challenges. This is an opportunity to showcase your alignment with Nerdwallet's values and your ability to foster a collaborative environment.

5. Final Interview

The final stage may involve a more in-depth discussion with senior team members or hiring managers. This interview often focuses on strategic thinking and your ability to act as a thought partner to product managers. You may be asked to discuss how you would approach specific projects or challenges within the data engineering domain, as well as your vision for enhancing data practices at Nerdwallet.

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 effectively within a team.

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

Master SQL and Data Modeling

Given the emphasis on SQL in the role, ensure you are well-versed in advanced SQL concepts, particularly analytical functions like RANK() and ROW_NUMBER(). Be prepared to demonstrate your understanding of complex queries and data modeling techniques. Practice coding problems that require you to manipulate and analyze data effectively, as this will likely be a significant part of your interview. Familiarize yourself with slowly changing dimensions and how they apply to data warehousing, as this knowledge will be beneficial.

Showcase Your Python Proficiency

While SQL is crucial, Python is also a key skill for a Data Engineer at NerdWallet. Brush up on your Python programming skills, focusing on data manipulation libraries such as Pandas and NumPy. Be ready to discuss how you have used Python in previous projects, particularly in building data pipelines or automating data processes. Highlight any experience you have with frameworks like Airflow, as this will demonstrate your ability to manage workflows effectively.

Understand the Company Culture

NerdWallet values collaboration, innovation, and inclusivity. During your interview, emphasize your ability to work well in teams and your commitment to fostering a positive work environment. Share examples of how you have contributed to team success in the past, whether through effective communication, constructive feedback, or collaborative problem-solving. This will resonate well with the company's culture and show that you are a good fit.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Be ready to discuss specific instances where you identified and resolved data issues, implemented process improvements, or collaborated with cross-functional teams. This will demonstrate your analytical thinking and your ability to drive results.

Be Ready for Technical Challenges

The interview process may include hands-on coding challenges or technical assessments. Practice coding under time constraints to simulate the interview environment. Focus on writing clean, efficient code and be prepared to explain your thought process as you work through problems. If you encounter difficulties, communicate your reasoning and approach to the interviewer, as this can showcase your problem-solving skills even if you don't arrive at the final solution.

Communicate Clearly and Effectively

Strong communication skills are essential for this role, as you will need to collaborate with various stakeholders. Practice articulating your thoughts clearly and concisely, especially when discussing technical concepts. Be prepared to explain complex data engineering topics in a way that is understandable to non-technical team members. This will demonstrate your ability to bridge the gap between technical and non-technical audiences.

Stay Informed About Industry Trends

Keep yourself updated on the latest trends and technologies in data engineering, such as advancements in cloud computing, data warehousing solutions, and data quality management. Being knowledgeable about industry best practices will not only help you answer questions more effectively but also show your enthusiasm for the field and your commitment to continuous learning.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at NerdWallet. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at NerdWallet. The interview will likely focus on your technical skills, particularly in SQL, data modeling, and Python, as well as your ability to work collaboratively and communicate effectively with cross-functional teams. Be prepared to demonstrate your understanding of data pipelines, data quality, and analytical functions.

SQL and Data Manipulation

1. Can you explain the difference between RANK() and ROW_NUMBER() in SQL?

Understanding the nuances between these two functions is crucial for data manipulation and reporting.

How to Answer

Discuss the specific use cases for each function, emphasizing how RANK() can produce ties while ROW_NUMBER() does not. Provide examples of scenarios where one would be preferred over the other.

Example

“RANK() assigns the same rank to identical values, which can lead to gaps in the ranking sequence, while ROW_NUMBER() provides a unique sequential number to each row regardless of ties. For instance, if I were ranking sales data, I would use RANK() to highlight top performers, but ROW_NUMBER() would be better for pagination in a report.”

2. How would you approach designing a slowly changing dimension in a data warehouse?

This question assesses your understanding of data modeling and how to manage historical data.

How to Answer

Explain the different types of slowly changing dimensions (Type 1, Type 2, Type 3) and provide a rationale for choosing one over the others based on business requirements.

Example

“I would typically use Type 2 for slowly changing dimensions when it’s important to preserve historical data. For example, if a customer’s address changes, I would create a new record with the new address while keeping the old record intact, allowing for accurate historical reporting.”

3. Describe a scenario where you had to troubleshoot a data pipeline issue. What steps did you take?

This question evaluates your problem-solving skills and your ability to maintain data integrity.

How to Answer

Outline a specific instance where you identified a problem, the steps you took to diagnose it, and how you resolved it, emphasizing collaboration with other teams if applicable.

Example

“Once, I noticed discrepancies in our sales data due to a failed ETL job. I first checked the logs to identify the error, then collaborated with the data engineering team to fix the underlying issue. After resolving it, I implemented monitoring alerts to prevent similar issues in the future.”

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

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

How to Answer

Discuss the strategies you employ to maintain data quality, such as validation checks, automated testing, and monitoring.

Example

“I implement data validation checks at various stages of the pipeline to catch errors early. Additionally, I use automated tests to verify data integrity after each transformation and set up monitoring dashboards to track data quality metrics over time.”

5. Can you explain how you would club a set of anagrams together from a list of words?

This question tests your algorithmic thinking and problem-solving skills.

How to Answer

Describe the approach you would take to group anagrams, possibly using data structures like dictionaries or sets.

Example

“I would iterate through the list of words, sorting each word alphabetically to create a key. I would then use a dictionary to group words with the same key together. This way, all anagrams would be collected under the same entry in the dictionary.”

Data Engineering Practices

1. What experience do you have with AWS and how have you utilized it in your data engineering projects?

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

How to Answer

Discuss specific AWS services you have used, such as S3, Redshift, or Glue, and how they contributed to your data engineering tasks.

Example

“I have extensively used AWS S3 for data storage and Redshift for data warehousing. In one project, I set up an ETL process using AWS Glue to extract data from S3, transform it, and load it into Redshift for analysis, which significantly improved our reporting capabilities.”

2. Describe your experience with data modeling. What methodologies do you prefer?

This question assesses your understanding of data architecture and design principles.

How to Answer

Explain the methodologies you are familiar with, such as star schema or snowflake schema, and provide examples of when you applied them.

Example

“I prefer using the star schema for its simplicity and performance benefits in querying. In a recent project, I designed a star schema for our sales data, which allowed for faster reporting and easier understanding for the business users.”

3. How do you handle performance tuning in SQL queries?

This question evaluates your ability to optimize data retrieval and processing.

How to Answer

Discuss techniques you use for performance tuning, such as indexing, query rewriting, or analyzing execution plans.

Example

“I often start by analyzing the execution plan to identify bottlenecks. I then consider adding indexes on frequently queried columns and rewriting complex joins to improve performance. For instance, I once optimized a slow-running report by indexing the date column, which reduced query time by over 50%.”

4. What tools or frameworks have you used for orchestrating data workflows?

This question assesses your experience with data pipeline orchestration tools.

How to Answer

Mention specific tools you have used, such as Apache Airflow or DBT, and describe how they fit into your workflow.

Example

“I have used Apache Airflow to orchestrate our ETL processes. It allows me to schedule tasks, monitor their execution, and handle dependencies effectively. For instance, I set up a DAG that runs nightly to refresh our reporting data, ensuring that stakeholders always have access to the latest information.”

5. How do you approach continuous improvement in data engineering practices?

This question evaluates your commitment to enhancing processes and practices.

How to Answer

Discuss your philosophy on continuous improvement and provide examples of initiatives you have led or participated in.

Example

“I believe in regularly reviewing our data engineering practices to identify areas for improvement. For example, I initiated a bi-weekly retrospective meeting where the team discusses challenges and brainstorms solutions, leading to the implementation of new tools that have streamlined our data processing workflows.”