The Huntington Data Analyst Interview Guide

Getting ready for a Data Analyst interview at The Huntington? The Huntington Data Analyst interview process typically spans several question topics and evaluates skills in areas like SQL, data analytics, data cleaning, business reporting, and communicating insights to diverse stakeholders. Interview preparation is essential for this role, as candidates are expected to demonstrate not only technical proficiency in analyzing large and complex datasets, but also the ability to automate processes, ensure data integrity, and translate findings into actionable recommendations that support financial and operational decision-making.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Analyst positions at The Huntington.
• Gain insights into The Huntington’s Data Analyst interview structure and process.
• Practice real The Huntington Data Analyst interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the The Huntington Data Analyst interview process, along with sample questions and preparation tips tailored to help you succeed.

The Huntington National Bank is a leading regional bank headquartered in Columbus, Ohio, serving individuals, small businesses, and large enterprises across the Midwest. The company provides a comprehensive range of financial products and services, including personal and commercial banking, lending, wealth management, and investment solutions. With a strong focus on integrity, customer service, and regulatory compliance, Huntington strives to create positive economic impact in its communities. As a Data Analyst within the Finance Data Group, you will support critical financial reporting, regulatory compliance, and data-driven decision making, directly contributing to the bank’s operational excellence and strategic objectives.

As a Data Analyst at The Huntington, you will play a key role within the Finance Data Group, partnering with teams such as Regulatory Reporting, FP&A, and Balance Sheet Management to support external filings and internal management analytics. Your responsibilities include analyzing large financial and risk data sets, automating processes, and ensuring data integrity across multiple systems. You will develop and enhance data management processes, create KPI metrics, and deliver actionable insights to optimize revenue, margin, and operational expenses. Collaboration with technology teams and stakeholders on OFSAA ecosystem projects is essential, as is producing business requirements and configuring financial modules to meet project needs. This role directly contributes to the accuracy and efficiency of Huntington’s financial reporting and decision-making.

2.1 Stage 1: Application & Resume Review

During the initial review, Huntington’s recruiting team assesses your resume for demonstrated expertise in SQL, analytics, and experience with financial data management, regulatory reporting, and risk systems—especially within banking or financial services. They look for hands-on experience with large datasets, data automation, and familiarity with business intelligence tools and compliance frameworks. Tailoring your resume to highlight these skills and quantifiable achievements in data analysis will help you stand out.

2.2 Stage 2: Recruiter Screen

This stage typically involves a phone call with a Huntington HR representative. The recruiter will confirm your interest in the Data Analyst role, discuss your background in financial analytics, and clarify your experience with SQL, data cleaning, and process automation. Expect questions about your career trajectory, motivation for joining Huntington, and ability to work with cross-functional teams. Prepare by clearly articulating your technical skills and how they align with Huntington’s finance data needs.

2.3 Stage 3: Technical/Case/Skills Round

This round is usually conducted via video conference and led by a hiring manager or a panel from the finance data group. You will be asked to demonstrate your proficiency in SQL through live coding or scenario-based questions involving data extraction, cleaning, and analysis. Expect case studies that require you to analyze large datasets, automate reporting processes, or design KPIs for financial and operational metrics. You may also be asked to discuss your approach to data quality, present insights tailored to specific audiences, and solve problems related to regulatory reporting or risk management. Preparing with real-world examples of your data projects and practicing clear, structured explanations will be beneficial.

2.4 Stage 4: Behavioral Interview

In this stage, you’ll meet with team members or managers who assess your interpersonal skills, communication style, and cultural fit within Huntington. You will discuss how you collaborate across functions, handle challenges in data projects, and communicate complex insights to non-technical stakeholders. Behavioral questions often focus on situations where you exceeded expectations, navigated regulatory or compliance issues, or managed multiple priorities in a finance environment. Prepare by reflecting on your experiences and practicing concise, impact-driven storytelling.

2.5 Stage 5: Final/Onsite Round

The final stage may be conducted onsite or virtually, typically involving a panel interview with senior leaders such as a VP or analytics director. You’ll be expected to synthesize your technical and analytical skills, demonstrate your ability to automate processes, and explain your approach to financial data integrity and reporting. This round may include scenario-based questions, deeper dives into your prior projects, and discussions about your fit for Huntington’s collaborative and compliance-driven environment. Review the company’s values and be ready to articulate your contributions to both technical and business outcomes.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all interview rounds, Huntington’s HR team will reach out with an offer. This stage includes discussions about compensation, benefits, and work arrangements (e.g., flexible office/remote options). Be prepared to negotiate based on your experience and market benchmarks, and clarify any questions about team structure or career progression.

2.7 Average Timeline

The Huntington Data Analyst interview process typically spans 2-4 weeks from application to offer, with some candidates receiving feedback within a week after their final interview. Fast-track applicants with highly relevant experience may progress more quickly, while standard timelines allow for scheduling flexibility and thorough evaluation. Panel interviews and technical rounds may be grouped in a single day or spread out over several days, depending on team availability.

Now, let’s dive into the types of interview questions you can expect throughout the Huntington Data Analyst process.

3.1 SQL & Data Manipulation

Data analysts at The Huntington are expected to demonstrate strong SQL skills and the ability to manipulate and analyze large datasets efficiently. Questions in this category will test your ability to write complex queries, optimize performance, and handle real-world data issues.

3.1.1 Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign
Use conditional aggregation or filtering to identify users who meet both criteria. Highlight your approach to efficiently scan large event logs.

3.1.2 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Describe your ETL process, including data profiling, cleaning, joining strategies, and methods for ensuring data quality and consistency.

3.1.3 How would you present the performance of each subscription to an executive?
Explain how you’d use SQL to aggregate metrics and create executive-level summaries, focusing on clarity and actionable insights.

3.1.4 Design a data pipeline for hourly user analytics.
Discuss building robust data pipelines, scheduling, and aggregation strategies to ensure timely and accurate reporting.

3.2 Analytics & Experimentation

This topic covers your ability to design experiments, measure impact, and interpret results using statistical and analytical methods. Expect questions on A/B testing, KPI selection, and translating business problems into analytical solutions.

3.2.1 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Describe how to structure an experiment, select relevant KPIs (e.g., conversion, retention, ROI), and monitor the promotion's impact.

3.2.2 We're interested in how user activity affects user purchasing behavior.
Explain how you’d analyze correlations or causal relationships, potentially using cohort analysis or regression techniques.

3.2.3 The role of A/B testing in measuring the success rate of an analytics experiment
Outline how you’d design an experiment, define success metrics, and ensure statistical significance.

3.2.4 Assessing the market potential and then use A/B testing to measure its effectiveness against user behavior
Discuss how to combine market analysis with experimental design, and how to interpret results for business recommendations.

3.3 Data Quality & Cleaning

Handling data quality issues is a critical part of the data analyst role. These questions evaluate your ability to identify, clean, and document data issues, as well as communicate the impact of data quality on business decisions.

3.3.1 How would you approach improving the quality of airline data?
Describe data profiling, validation rules, and feedback loops to ensure ongoing data quality improvements.

3.3.2 Describing a real-world data cleaning and organization project
Share a systematic approach to profiling, cleaning, and documenting messy datasets, and how you ensured reproducibility.

3.3.3 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Explain how you’d restructure data for analysis, addressing missing values, inconsistent formats, and layout issues.

3.3.4 Modifying a billion rows
Discuss efficient strategies for handling large-scale data updates, such as batching, indexing, and minimizing downtime.

3.4 Data Communication & Visualization

Expect to be tested on your ability to communicate insights clearly to both technical and non-technical audiences. This includes designing effective dashboards, visualizations, and written summaries that drive business action.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Focus on tailoring your message, using visuals, and adjusting technical depth based on your audience’s needs.

3.4.2 Demystifying data for non-technical users through visualization and clear communication
Discuss strategies for making data accessible, such as using intuitive charts, storytelling, and interactive dashboards.

3.4.3 Making data-driven insights actionable for those without technical expertise
Explain how you break down complex findings into clear, actionable takeaways and use analogies when appropriate.

3.4.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Describe your approach to summarizing and visualizing skewed or text-heavy data for maximum impact.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision. What was the outcome, and how did you ensure your recommendation was implemented?

3.5.2 Describe a challenging data project and how you handled it, including any obstacles and your approach to overcoming them.

3.5.3 How do you handle unclear requirements or ambiguity when starting a new analysis?

3.5.4 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.

3.5.5 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.

3.5.6 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.

3.5.7 Describe a time you had to negotiate scope creep when multiple teams kept adding new requests to your analytics project. How did you keep the project on track?

3.5.8 Tell us about a time you delivered critical insights even though a significant portion of the dataset had missing or unreliable values. What trade-offs did you make?

3.5.9 Explain how you communicated uncertainty to executives when your cleaned dataset covered only part of the data you needed.

3.5.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.

4.1 Company-specific tips:

Familiarize yourself with The Huntington’s core business pillars, especially its focus on financial services, regulatory compliance, and risk management. Understanding how the bank serves its customers—from personal banking to commercial lending—will help you contextualize data problems and better anticipate the kinds of insights that drive decision-making at Huntington.

Study Huntington’s recent financial reports, regulatory filings, and key performance indicators (KPIs) such as revenue growth, margin optimization, and operational expense management. This will prepare you to discuss how your analysis can support these objectives and demonstrate your awareness of the bank’s strategic priorities.

Get to know the Finance Data Group’s role within Huntington, including how it supports functions like FP&A, Regulatory Reporting, and Balance Sheet Management. Be ready to articulate how data analytics contributes to external filings, internal management analytics, and compliance with industry standards.

Review Huntington’s values and culture, emphasizing integrity, customer-centricity, and collaboration. Prepare examples that show how you embody these values in your work, especially when navigating complex data challenges or working with cross-functional teams.

4.2 Role-specific tips:

4.2.1 Practice SQL skills focused on analyzing large financial datasets and automating reporting processes.
Be ready to write and explain complex SQL queries that extract, clean, and aggregate financial data. Demonstrate your understanding of how to optimize queries for performance, handle data from multiple sources, and automate routine reporting tasks to improve efficiency and accuracy.

4.2.2 Prepare to discuss your approach to data cleaning and ensuring data integrity across multiple systems.
Showcase your experience in profiling, cleaning, and validating messy or incomplete datasets, especially those relevant to banking and finance. Be prepared to explain how you document your process, establish validation rules, and ensure reproducibility—highlighting your commitment to data quality.

4.2.3 Illustrate your ability to design and implement data pipelines for timely analytics.
Talk through real-world examples of building robust data pipelines that support hourly or daily reporting. Discuss your strategies for scheduling, aggregation, and error handling, and emphasize how your solutions ensure timely and accurate delivery of insights to stakeholders.

4.2.4 Demonstrate your proficiency in translating complex analysis into actionable business recommendations.
Practice communicating technical findings in clear, concise language tailored to both executives and non-technical stakeholders. Use storytelling, visualization, and executive summaries to make your insights accessible and actionable, focusing on how your recommendations support Huntington’s business goals.

4.2.5 Highlight your experience with regulatory reporting and compliance-driven analytics.
Share specific examples of working on projects that required strict adherence to regulatory requirements, such as preparing data for external filings or supporting risk management initiatives. Explain how you ensure compliance, track changes, and collaborate with regulatory teams.

4.2.6 Be ready to discuss your collaboration skills with cross-functional teams, including technology and finance.
Prepare stories that showcase your ability to work with diverse groups, align on business requirements, and manage competing priorities. Emphasize your role in bridging gaps between technical and business stakeholders, and how you contribute to successful project outcomes.

4.2.7 Reflect on your experience managing ambiguity and scope creep in analytics projects.
Think about times when you’ve navigated unclear requirements, shifting priorities, or expanding project scopes. Share your strategies for clarifying objectives, negotiating deliverables, and keeping projects on track while maintaining data integrity and stakeholder satisfaction.

4.2.8 Prepare examples of automating data-quality checks and recurring analytics processes.
Show your initiative in developing automated solutions for data validation and quality assurance. Discuss how these efforts have helped prevent data issues, improved reporting reliability, and saved time for your teams.

4.2.9 Practice explaining trade-offs and uncertainty in data-driven decisions.
Be ready to discuss situations where you had to make recommendations based on incomplete or imperfect data. Focus on how you communicated risks, quantified uncertainty, and helped stakeholders make informed decisions despite limitations.

4.2.10 Showcase your ability to create and visualize dashboards that drive action.
Describe your approach to designing dashboards for executive and operational audiences, selecting the right metrics, and presenting data in a way that encourages clear decision-making. Highlight your skills in summarizing long-tail or text-heavy data for maximum impact.

5.1 How hard is the The Huntington Data Analyst interview?
The Huntington Data Analyst interview is moderately challenging, especially for candidates with limited experience in financial analytics and regulatory reporting. You’ll be evaluated on your ability to manipulate large datasets, automate reporting processes, and communicate insights to both technical and non-technical stakeholders. The most demanding aspects involve real-world case studies and scenario-based SQL questions, as well as behavioral interviews that assess your collaboration and communication skills. Candidates with hands-on experience in banking, finance, or compliance-driven environments will find themselves well-prepared.

5.2 How many interview rounds does The Huntington have for Data Analyst?
Typically, The Huntington’s Data Analyst interview process consists of five to six rounds: an initial application and resume review, a recruiter screen, a technical/case round, a behavioral interview, and a final panel or onsite round. Some candidates may also encounter a take-home assignment or additional technical interviews, depending on the team’s requirements and your background.

5.3 Does The Huntington ask for take-home assignments for Data Analyst?
Yes, it’s common for The Huntington to include a take-home assignment as part of the Data Analyst interview process. These assignments usually focus on analyzing financial or operational datasets, automating reporting processes, or designing KPIs relevant to Huntington’s business. You’ll be asked to demonstrate your technical skills, problem-solving abilities, and how you communicate your findings in a clear, actionable format.

5.4 What skills are required for The Huntington Data Analyst?
Key skills for The Huntington Data Analyst include advanced SQL, data cleaning and profiling, experience with financial and risk data, business intelligence reporting, and process automation. Strong communication skills are essential for translating complex analysis into actionable recommendations for finance and non-technical stakeholders. Familiarity with regulatory reporting, compliance frameworks, and collaborative project management in banking or financial services is highly valued.

5.5 How long does the The Huntington Data Analyst hiring process take?
The Huntington Data Analyst hiring process typically takes 2-4 weeks from application to offer. Fast-track candidates with highly relevant experience may progress more quickly, while the standard timeline allows for multiple rounds, panel interviews, and scheduling flexibility. You can expect feedback within a week after your final interview, though timing can vary depending on team availability.

5.6 What types of questions are asked in the The Huntington Data Analyst interview?
Expect a mix of technical SQL and data manipulation questions, analytics case studies, and scenario-based problems involving financial reporting, regulatory compliance, and risk management. You’ll also face behavioral questions about collaboration, managing ambiguity, and communicating insights to diverse audiences. Questions will test your ability to automate processes, ensure data integrity, and deliver actionable business recommendations.

5.7 Does The Huntington give feedback after the Data Analyst interview?
The Huntington typically provides high-level feedback through recruiters after the interview process. While you may receive general insights on your strengths and areas for improvement, detailed technical feedback is less common. If you reach the final round, the feedback is often more specific regarding your fit for the team and role.

5.8 What is the acceptance rate for The Huntington Data Analyst applicants?
The exact acceptance rate is not publicly disclosed, but the Data Analyst role at The Huntington is competitive, with an estimated acceptance rate of 3-7% for qualified applicants. Candidates with strong financial analytics experience, regulatory reporting skills, and a track record of collaborative project delivery have a higher chance of success.

5.9 Does The Huntington hire remote Data Analyst positions?
Yes, The Huntington offers remote Data Analyst positions, especially within the Finance Data Group. Some roles may require occasional office visits for team collaboration or project meetings, but flexible work arrangements are increasingly common. Be sure to clarify remote work expectations during the offer and negotiation stage.

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