and Huntington Data Engineer interview questions & guide 2026

The following questions are representative of the patterns observed in recent candidate experiences. While your specific interview may vary based on the team’s current focus—whether it be cloud migration or legacy warehouse maintenance—these categories reflect the core competencies and Huntington prioritizes.

Technical & Domain Expertise

These questions test your proficiency with the specific tools and infrastructure required for the Data Engineer position. Expect to demonstrate your practical application of these technologies.

• What is your background in Python, AWS, and Glue?
• How do you design and optimize ETL pipelines for large-scale data warehouses?

Preparation for and Huntington requires a balanced approach. You must be technically sharp, but you should also be prepared to articulate your experience in a way that aligns with the bank's operational standards.

Technical Proficiency – Interviewers will look for hands-on experience with AWS, Python, and ETL frameworks. You should be ready to discuss specific projects where you utilized these tools to solve business problems, focusing on the "how" and "why" of your technical decisions.

Problem-Solving Ability – You will be evaluated on your ability to break down complex data challenges into manageable tasks. Frame your answers by identifying the business problem, the technical constraints you faced, and the specific architecture you implemented to resolve the issue.

Communication & Collaboration – As a Data Engineer, you are a bridge between technical systems and business needs. You must demonstrate that you can explain technical concepts to non-technical stakeholders and work effectively within a team-oriented, hierarchical organization.

The interview process at and Huntington is typically structured, professional, and consistent with standard corporate hiring practices. You should anticipate a process that moves from an initial assessment of your background and compensation requirements to a more technical evaluation of your skills.

The journey usually begins with a phone screen conducted by a recruiter. This stage is designed to verify your credentials, discuss your interest in the bank, and ensure your salary expectations align with the established ranges. Because the bank values transparency regarding compensation and role structure, expect this to be a core part of the early conversation.

This visual timeline illustrates the typical progression from an initial recruiter screen to potential follow-up interviews. Use this to pace your preparation, ensuring you have your professional narrative ready for the recruiter while keeping your technical deep-dives focused on AWS and ETL for later stages.

Cloud Data Infrastructure

The bank is increasingly reliant on cloud-native solutions. You will be evaluated on your ability to deploy and manage data within AWS environments.

Be ready to go over:

• AWS Glue – Understanding how to use it for serverless data integration.
• Data Migration – Strategies for moving data from on-premise systems to the cloud.

As a Data Engineer or Lead ETL Developer, your primary responsibility is the health and efficiency of the Enterprise Data Warehouse. You will design and code complex data pipelines that extract data from disparate sources, transform it into usable formats, and load it into the warehouse for downstream consumption.

You will work closely with other data engineers, architects, and business analysts. A significant portion of your work involves troubleshooting data quality issues, ensuring that the warehouse remains a "single source of truth." You will also be expected to contribute to the long-term strategy of the data platform, helping the team decide when to modernize legacy processes versus when to maintain existing, stable workflows.

A competitive candidate for this role will demonstrate both depth in core engineering skills and the ability to operate within a large, established enterprise.

• Must-have skills – Proficient in Python, substantial experience with ETL/ELT development, and hands-on expertise with AWS services like Glue or Redshift.
• Nice-to-have skills – A graduate degree in a technical field is often preferred, alongside experience in the financial services sector, which helps in understanding regulatory data requirements.
• Soft skills – Strong documentation habits and the ability to work within a team-based, project-driven environment are essential for success.

Q: How long does the interview process typically take? The process is generally efficient, though it can vary based on team requirements. Expect a few weeks from the initial screen to a final decision.

Q: Is there a heavy emphasis on coding tests? The focus is often on practical application and architecture rather than complex algorithmic puzzles. Be prepared to discuss your code and design patterns in detail.

Q: What is the work culture like? and Huntington values stability, structure, and collaboration. You will find that the team is focused on delivering reliable, high-quality data solutions in a professional environment.

Q: Are there remote or hybrid options? Most roles in this department are based in Columbus, OH, and follow the company's specific office-attendance policies. Confirm the current policy with your recruiter during the initial screen.

• Understand the stack: Research how AWS services are specifically used in a banking context, focusing on security and data governance.
• Be ready for salary discussions: The bank is transparent about compensation; be prepared to discuss your expectations clearly during the first call.
• Highlight your impact: Don't just list what you did; explain how your work improved data latency, accuracy, or cost-efficiency.

The Data Engineer position at and Huntington offers a unique opportunity to shape the data architecture of a major financial institution. By focusing on your technical proficiency in AWS and Python, while demonstrating a clear understanding of the business impact of your work, you will position yourself as a strong candidate. Remember to leverage Dataford to explore additional interview insights, practice questions, and preparation resources as you refine your approach.

The provided salary data reflects the market range for this role and its associated seniority levels. Use this information to benchmark your expectations and ensure you are prepared to discuss compensation professionally during the early stages of the interview process. Focus on your value proposition—your technical skill set and ability to handle large-scale data—to negotiate effectively.