National Funding Data Engineer Interview Questions + Guide in 2025

National Funding is a leading provider of financial services focused on empowering small businesses across the United States by offering tailored solutions to meet their funding needs.

As a Data Engineer at National Funding, you will play a crucial role in shaping the data landscape of the company by ensuring the integrity and utility of data across multiple systems. Your key responsibilities will include participating in the full data warehouse lifecycle—from requirements gathering and data modeling to ETL (Extract, Transform, Load) processes—using tools like dbt and AWS. You will also collaborate closely with data analysts, data scientists, and business stakeholders to create a seamless and efficient data environment that supports modeling, analytics, and reporting.

To excel in this role, you should possess a solid background in data engineering, with a minimum of 5 years of experience in data warehousing and ELT processes. Proficiency in dbt, Snowflake, and AWS is essential, alongside expertise in data governance and data modeling. You should also be comfortable managing CI/CD pipelines and addressing operational challenges swiftly and effectively. An ideal candidate is detail-oriented, analytical, and demonstrates a passion for helping small businesses thrive through data-driven insights.

This guide will help you prepare for your interview by providing insights into the specific skills and experiences that are highly valued at National Funding, allowing you to tailor your responses to align with the company's goals and expectations.

The interview process for a Data Engineer at National Funding is structured to assess both technical skills and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different aspects of a candidate's qualifications and experience.

1. Initial Phone Screen

The process begins with a phone interview conducted by a recruiter. This initial call usually lasts about 30 minutes and focuses on your background, experience, and understanding of the role. The recruiter will also provide insights into the company culture and expectations for the position. This is an opportunity for you to express your interest in the role and ask any preliminary questions you may have.

2. Technical Interview

Following the initial screen, candidates typically participate in a technical interview, which may be conducted via video call. This interview is often led by a hiring manager or a senior data engineer. During this session, you can expect to answer questions related to your experience with SQL, ETL processes, and data modeling. You may also be asked to solve technical problems or discuss your approach to data integration and transformation using tools like dbt and AWS.

3. Panel Interview

The next step in the process is usually a panel interview, which takes place in person. This round typically involves multiple team members, including the Director of Data Engineering and other data engineers. The panel will assess your technical skills further, focusing on your ability to work collaboratively and your understanding of data governance and best practices. Expect questions that require you to demonstrate your knowledge of data warehousing, ELT processes, and your experience with tools like Snowflake and git.

4. Cultural Fit Interview

In some cases, candidates may also have a cultural fit interview, which is often a more informal discussion with team members or management. This round aims to evaluate how well you align with the company's values and work environment. You may be asked about your work style, how you handle challenges, and your approach to teamwork and collaboration.

5. Final Interview

The final stage may involve a conversation with higher-level executives, such as the Chief Data Officer or the President. This interview is typically shorter and serves as a final check to ensure that you are a good fit for the organization as a whole. It may also cover your long-term career goals and how they align with the company's vision.

As you prepare for your interviews, be ready to discuss your technical expertise and past experiences in detail, as well as your approach to problem-solving and collaboration.

Next, let's delve into the specific interview questions that candidates have encountered during the process.

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

Understand the Interview Structure

The interview process at National Funding typically consists of multiple rounds, including a recruiter phone screen, a hiring manager interview, and a panel interview. Familiarize yourself with this structure and prepare accordingly. Knowing that you may meet with various stakeholders, including the Director of Data Engineering and team members, will help you tailor your responses to their specific interests and concerns.

Highlight Your Technical Expertise

Given the emphasis on SQL, dbt, and AWS in the role, ensure you can discuss your experience with these technologies in detail. Be prepared to answer questions about your past projects involving data warehousing, ETL processes, and data governance. You may also be asked to demonstrate your knowledge of data modeling and how you have applied business rules and transformations in your previous roles.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving skills and ability to work in a team. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Highlight instances where you successfully collaborated with analysts, data scientists, or business stakeholders to achieve a common goal, especially in the context of data integration and reporting.

Familiarize Yourself with Company Culture

National Funding prides itself on a positive, energetic, and passionate work environment. During your interview, convey your enthusiasm for contributing to a team that values collaboration and innovation. Share examples of how you have thrived in similar environments and how you can contribute to the company’s growth and success.

Be Ready for Technical Assessments

You may encounter technical assessments or case studies during the interview process. Brush up on your SQL skills, particularly with complex queries, window functions, and data transformations. Additionally, be prepared to discuss your experience with dbt and Snowflake, as these are critical tools for the role. Practicing with sample problems or case studies can help you feel more confident.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers. Inquire about the current data initiatives at National Funding, the team dynamics, and how the data engineering department supports the company's overall goals. This not only shows your interest in the role but also helps you gauge if the company aligns with your career aspirations.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the position and briefly mention how your skills align with the company’s needs. This small gesture can leave a positive impression and keep you top of mind as they make their hiring decision.

By following these tips, you can present yourself as a well-prepared and enthusiastic candidate who is ready to contribute to National Funding's data engineering team. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at National Funding. The interview process will focus on your technical skills, particularly in data engineering, ETL processes, and tools like SQL, dbt, and AWS. Be prepared to discuss your experience with data modeling, data warehousing, and any relevant projects you've worked on.

Technical Skills

1. Can you explain the ETL process and how you have implemented it in your previous projects?

Understanding the ETL process is crucial for a Data Engineer role. Be specific about your experience and the tools you used.

How to Answer

Discuss the steps of ETL (Extract, Transform, Load) and provide examples of how you have applied these steps in your previous roles, including any challenges you faced and how you overcame them.

Example

“In my last role, I implemented an ETL process using dbt to extract data from various sources, transform it by applying business rules, and load it into our Snowflake data warehouse. One challenge was ensuring data quality during the transformation phase, which I addressed by implementing rigorous testing and validation procedures.”

2. What is your experience with dbt, and how have you used it in your projects?

Dbt is a key tool for data transformation, and familiarity with it is essential for this role.

How to Answer

Explain your experience with dbt, including specific projects where you utilized it, and highlight any advanced features you used.

Example

“I have used dbt extensively to create modular SQL transformations and manage our data models. In one project, I set up a dbt pipeline that automated our data transformations, which reduced processing time by 30% and improved data accuracy.”

3. How do you approach data modeling, and what techniques do you use?

Data modeling is a fundamental skill for a Data Engineer, and interviewers will want to know your approach.

How to Answer

Discuss the different types of data models you have worked with (e.g., star schema, snowflake schema) and your process for creating them.

Example

“I typically start with a requirements gathering phase to understand the business needs, followed by creating a conceptual model. I prefer using a star schema for reporting purposes due to its simplicity and performance benefits. In my last project, I designed a star schema that improved query performance significantly.”

4. Can you describe your experience with AWS and how you have utilized its services in data engineering?

AWS is a critical component of the data infrastructure at National Funding, so familiarity with its services is important.

How to Answer

Mention specific AWS services you have used (e.g., S3, Redshift, Lambda) and how they fit into your data engineering workflows.

Example

“I have used AWS S3 for data storage and Redshift for data warehousing. In a recent project, I set up an automated pipeline that ingested data from S3 into Redshift using AWS Glue, which streamlined our data processing and reduced manual intervention.”

Data Governance and Quality

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

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

How to Answer

Discuss the methods you use to validate data and ensure its integrity throughout the ETL process.

Example

“I implement data validation checks at each stage of the ETL process, including schema validation and data type checks. Additionally, I use logging and monitoring tools to track data quality metrics and quickly identify any anomalies.”

6. What strategies do you use for data governance in your projects?

Data governance is essential for compliance and data management, so be prepared to discuss your approach.

How to Answer

Explain your understanding of data governance principles and how you have applied them in your work.

Example

“I follow best practices for data governance by establishing clear data ownership and access controls. In my previous role, I implemented a data catalog to document data sources and lineage, which improved transparency and compliance with data regulations.”

Problem-Solving and Collaboration

7. Describe a challenging data engineering problem you faced and how you resolved it.

Interviewers want to assess your problem-solving skills and ability to handle challenges.

How to Answer

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

Example

“In one project, we faced performance issues with our data pipeline due to large data volumes. I analyzed the bottlenecks and optimized our ETL process by partitioning the data and using incremental loads, which improved performance by over 50%.”

8. How do you collaborate with data scientists and analysts in your role?

Collaboration is key in data engineering, and interviewers will want to know how you work with other teams.

How to Answer

Discuss your experience working with cross-functional teams and how you ensure alignment on data needs.

Example

“I regularly collaborate with data scientists and analysts to understand their data requirements. I hold weekly meetings to discuss ongoing projects and gather feedback on data quality, which helps us align our efforts and improve the overall data ecosystem.”