Lehigh University Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Lehigh University? The Lehigh University Business Intelligence interview process typically spans a broad range of question topics and evaluates skills in areas like data analytics, SQL, dashboard design, experiment measurement, and communicating actionable insights to diverse audiences. Interview preparation is especially important for this role at Lehigh University, as candidates are expected to analyze complex datasets, design and build scalable data solutions, and present findings that drive decision-making across academic and administrative functions.

In preparing for the interview, you should:

• Understand the core skills necessary for Business Intelligence positions at Lehigh University.
• Gain insights into Lehigh University’s Business Intelligence interview structure and process.
• Practice real Lehigh University Business Intelligence 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 Lehigh University Business Intelligence interview process, along with sample questions and preparation tips tailored to help you succeed.

Lehigh University is a nationally recognized private research university located in Bethlehem, Pennsylvania, known for its strong programs in engineering, business, and the sciences. Serving over 7,000 undergraduate and graduate students, Lehigh emphasizes interdisciplinary learning, innovation, and experiential education. The university is committed to advancing knowledge and fostering leadership through research, scholarship, and community engagement. As part of the Business Intelligence team, you will contribute to data-driven decision-making that supports Lehigh’s academic mission and operational effectiveness.

As a Business Intelligence professional at Lehigh University, you will be responsible for transforming institutional data into actionable insights that support strategic decision-making across academic and administrative departments. Your work will involve gathering, analyzing, and visualizing complex data sets, developing interactive dashboards, and generating reports for stakeholders. You will collaborate with IT, finance, enrollment, and other campus teams to identify trends, improve operational efficiency, and guide resource allocation. This role is key in helping the university optimize processes, enhance student outcomes, and achieve its institutional goals through data-driven strategies.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application and resume, focusing on your experience in business intelligence, data analytics, and your ability to work with complex data systems. The hiring team looks for proficiency in SQL, data warehousing, ETL pipelines, dashboard development, and evidence of presenting insights to non-technical stakeholders. Highlighting past projects involving data cleaning, integration of multiple data sources, and actionable business recommendations will help your application stand out.

2.2 Stage 2: Recruiter Screen

This initial conversation is typically conducted by a university HR representative or recruiting coordinator. The call lasts about 30 minutes and explores your motivation for applying, overall fit with the university’s mission, and your background in business intelligence. Expect to discuss your career trajectory, communication skills, and how you make data accessible to diverse audiences. Preparation should involve articulating your interest in higher education analytics and demonstrating your ability to translate data insights into strategic decisions.

2.3 Stage 3: Technical/Case/Skills Round

Led by a member of the analytics or business intelligence team, this round assesses your technical expertise and problem-solving abilities. You may be asked to design data warehouses, build ETL pipelines, write SQL queries, analyze multiple data sources, and model business scenarios such as merchant acquisition or campaign effectiveness. The interview may include case studies that require you to present complex findings, measure experiment success (e.g., A/B testing), and design dashboards tailored for executive decision-making. Preparation should focus on refreshing your skills in data modeling, analytics, and system design, as well as practicing clear, audience-specific communication.

2.4 Stage 4: Behavioral Interview

Conducted by the hiring manager or cross-functional team members, this stage evaluates your interpersonal skills, adaptability, and approach to collaboration. Expect to discuss challenges faced during data projects, experiences in presenting insights to non-technical users, and how you handle ambiguity or conflicting priorities. Prepare to share examples of teamwork, leadership, and how you drive business outcomes through data-driven recommendations.

2.5 Stage 5: Final/Onsite Round

The final stage typically involves a series of in-person or virtual interviews with key stakeholders, including the analytics director, business intelligence manager, and potential cross-departmental partners. This round may include a technical presentation, a deep-dive into your portfolio, and situational exercises focusing on system design, data quality assurance, and strategic impact. You’ll be assessed on your ability to communicate technical concepts, influence decision-making, and demonstrate thought leadership in business intelligence.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll enter negotiations with the HR team regarding compensation, benefits, and start date. This stage is straightforward and typically involves clarifying the terms of employment and finalizing paperwork.

2.7 Average Timeline

The typical interview process for a Business Intelligence role at Lehigh University spans 3-5 weeks from initial application to offer. Fast-track candidates with highly relevant experience or internal referrals may progress in 2-3 weeks, while standard pacing allows a week or more between each stage to accommodate scheduling and feedback cycles. The technical and onsite rounds often require advance preparation and may be scheduled over several days, depending on stakeholder availability.

Next, let’s explore the specific interview questions you may encounter throughout these stages.

3.1 Data Analysis & Business Impact

Business Intelligence at Lehigh University involves transforming data into actionable insights that drive strategic decisions. Expect questions that assess your ability to analyze data, evaluate business opportunities, and communicate findings to both technical and non-technical stakeholders.

3.1.1 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?
Explain how you would design an experiment or analysis to assess the impact of a promotion, including identifying key metrics such as revenue, retention, and customer acquisition. Discuss how you would use data to make a recommendation.

3.1.2 How would you design a system that offers college students with recommendations that maximize the value of their education?
Describe your approach to identifying relevant metrics and data sources, and how you’d use analytics to drive personalized recommendations. Emphasize the importance of aligning system outputs with student success outcomes.

3.1.3 How would you measure the success of an email campaign?
Outline the process of defining key performance indicators (KPIs) such as open rates, click-through rates, and conversions, and how you’d use these to assess campaign effectiveness.

3.1.4 How would you analyze how the feature is performing?
Discuss your method for tracking user engagement, conversions, and other relevant metrics to evaluate a new feature’s impact.

3.1.5 Let’s say that you're in charge of an e-commerce D2C business that sells socks. What business health metrics would you care?
List and justify the core metrics (e.g., customer lifetime value, churn, average order value) you’d monitor to gauge the business’s overall health.

3.2 Data Modeling & Experimentation

For Business Intelligence roles, you’ll be expected to design experiments, build data models, and interpret results to inform business strategy. These questions test your understanding of A/B testing, causal inference, and the design of robust analytics solutions.

3.2.1 The role of A/B testing in measuring the success rate of an analytics experiment
Describe how you would use A/B testing to compare different strategies, including how to set up control and treatment groups and interpret statistical significance.

3.2.2 How would you establish causal inference to measure the effect of curated playlists on engagement without A/B?
Explain alternative methods for causal inference, such as difference-in-differences or propensity score matching, and discuss their limitations.

3.2.3 Assessing the market potential and then use A/B testing to measure its effectiveness against user behavior
Share how you would estimate market size, design an experiment, and use behavioral data to measure product impact.

3.2.4 Write a query to calculate the conversion rate for each trial experiment variant
Demonstrate your approach to aggregating experimental data, calculating conversion rates, and comparing results across groups.

3.2.5 How would you approach acquiring 1,000 riders for a new ride-sharing service in a small city?
Discuss how you’d use data to set targets, segment the market, and measure the effectiveness of different acquisition strategies.

3.3 Data Engineering & ETL

Business Intelligence professionals must often design data pipelines and ensure data quality. These questions evaluate your understanding of ETL processes, data warehouse design, and scalable solutions for large or complex datasets.

3.3.1 Design a data warehouse for a new online retailer
Describe your approach to data modeling, schema design, and the selection of key tables and fields to support analytics.

3.3.2 Ensuring data quality within a complex ETL setup
Explain the steps you’d take to validate data, monitor for errors, and implement quality assurance throughout the ETL process.

3.3.3 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Outline how you’d handle data ingestion, transformation, and loading for disparate data sources, with a focus on scalability and reliability.

3.3.4 Write a query to get the current salary for each employee after an ETL error.
Demonstrate your approach to correcting and auditing data after a pipeline error, including how you’d validate the results.

3.3.5 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Discuss the architecture you’d use, including data sources, transformation logic, and serving layer for predictive analytics.

3.4 Communication & Data Visualization

Clear communication of insights is crucial for Business Intelligence. These questions assess your ability to present data, tailor messages to different audiences, and make technical findings accessible.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe how you adjust your communication style and visualizations depending on the audience’s technical background.

3.4.2 Making data-driven insights actionable for those without technical expertise
Explain strategies for simplifying complex analytics and ensuring your recommendations are understood and actionable.

3.4.3 Demystifying data for non-technical users through visualization and clear communication
Share how you use visualization tools and storytelling techniques to bridge the gap between data and decision-makers.

3.4.4 Which metrics and visualizations would you prioritize for a CEO-facing dashboard during a major rider acquisition campaign?
Discuss how you select and design high-level dashboards for executive audiences, focusing on clarity and relevance.

3.5 Data Cleaning & Integration

Data quality and integration are foundational for reliable business intelligence. Expect questions about your experience with cleaning, merging, and profiling data from multiple sources.

3.5.1 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 workflow for data profiling, cleaning, joining, and ensuring consistency across datasets.

3.5.2 Describing a real-world data cleaning and organization project
Explain the challenges you faced, your approach to resolving them, and the impact on the final analysis.

3.5.3 Write a SQL query to count transactions filtered by several criterias.
Showcase your ability to write efficient queries that filter and aggregate transactional data according to business needs.

3.5.4 What kind of analysis would you conduct to recommend changes to the UI?
Discuss how you’d use user journey data to identify pain points and opportunities for UI improvements.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a specific instance where your analysis directly influenced a business outcome, detailing the data, your process, and the resulting impact.

3.6.2 Describe a challenging data project and how you handled it.
Highlight the obstacles you faced, your approach to overcoming them, and what you learned from the experience.

3.6.3 How do you handle unclear requirements or ambiguity?
Discuss your process for clarifying objectives, communicating with stakeholders, and iterating on deliverables.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Share an example where you adapted your communication style or tools to bridge gaps and ensure understanding.

3.6.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Explain how you managed expectations, prioritized requests, and communicated trade-offs to maintain project focus.

3.6.6 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Provide an example of how you built credibility, used evidence, and fostered alignment to drive decision-making.

3.6.7 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Describe how you identified the mistake, communicated transparently, and implemented safeguards to prevent recurrence.

3.6.8 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Discuss how you leveraged visual tools and iterative feedback to achieve consensus.

3.6.9 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Explain the automation you implemented, the problem it solved, and the resulting benefits for your team.

3.6.10 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Describe your approach to time management, prioritization frameworks, and tools for staying on top of competing demands.

4.1 Company-specific tips:

Familiarize yourself with Lehigh University’s core values, academic mission, and commitment to data-driven decision-making. Understand how business intelligence supports both academic and administrative functions, such as enrollment management, student outcomes, and resource allocation. Research Lehigh’s strategic initiatives, especially those focused on innovation, interdisciplinary collaboration, and operational efficiency. Be prepared to discuss how your work as a business intelligence professional can help the university achieve its goals and foster a culture of continuous improvement.

Demonstrate an understanding of the unique challenges and opportunities within higher education analytics. Highlight your awareness of the types of data Lehigh University collects and utilizes, such as student performance, financial data, research metrics, and operational statistics. Show that you appreciate the importance of bridging data silos and facilitating collaboration across departments to maximize institutional impact.

4.2 Role-specific tips:

4.2.1 Practice designing and analyzing experiments tailored to academic and administrative environments.
Be ready to discuss how you would set up A/B tests or other experimental designs to measure the impact of new initiatives, such as student engagement programs or process improvements. Focus on metrics relevant to higher education, like retention rates, enrollment conversion, and resource utilization. Articulate how you would interpret results and translate them into actionable recommendations for university leadership.

4.2.2 Strengthen your SQL and data modeling skills for complex, multi-source university datasets.
Sharpen your ability to write advanced SQL queries that aggregate, filter, and join data across diverse sources, such as admissions, course enrollment, and financial aid systems. Practice designing data warehouses and ETL pipelines that support scalable analytics and reporting. Be prepared to discuss your approach to maintaining data quality, resolving inconsistencies, and auditing for errors in large institutional datasets.

4.2.3 Prepare to communicate insights to both technical and non-technical stakeholders in an academic setting.
Develop strategies for presenting complex findings in a clear, accessible manner. Tailor your communication style and visualizations to the audience, whether it’s faculty, administrators, or executive leadership. Practice simplifying technical concepts and making recommendations that are easy to understand and act upon, especially for those with limited data literacy.

4.2.4 Showcase your ability to clean, integrate, and analyze data from multiple university systems.
Be ready to describe your workflow for profiling, cleaning, and merging data from sources such as student records, financial transactions, and research outputs. Emphasize your experience with handling messy or incomplete data and your methods for ensuring consistency and reliability. Prepare examples where your data integration work led to improved decision-making or operational efficiency.

4.2.5 Demonstrate experience with dashboard design and actionable reporting for academic leadership.
Practice building dashboards that highlight key performance indicators relevant to university operations—such as enrollment trends, graduation rates, and budget utilization. Focus on designing visualizations that enable quick, informed decision-making for executives and department heads. Be prepared to discuss your process for selecting metrics, prioritizing information, and iterating on dashboard designs based on stakeholder feedback.

4.2.6 Prepare real-world stories of influencing decision-making and driving change through data.
Gather examples from your experience where your analysis led to strategic shifts or process improvements. Highlight situations where you influenced stakeholders without formal authority, built consensus around data-driven recommendations, or used prototypes and wireframes to align diverse teams. Articulate the impact your work had on organizational outcomes and how you fostered a culture of evidence-based decision-making.

4.2.7 Be ready to discuss your approach to managing multiple projects, deadlines, and competing priorities.
Share your strategies for time management, prioritizing tasks, and staying organized when balancing several deliverables. Highlight any frameworks, tools, or habits you use to ensure timely completion of high-quality work. Emphasize your ability to adapt to shifting requirements and maintain focus on institutional goals.

4.2.8 Practice transparency and accountability in handling errors or data quality issues.
Prepare to talk about times when you caught mistakes in your analysis or reporting after sharing results. Explain how you communicated the error, corrected it, and implemented safeguards to prevent recurrence. Demonstrate your commitment to data integrity and continuous improvement, especially in high-stakes environments where accuracy is critical.

5.1 How hard is the Lehigh University Business Intelligence interview?
The Lehigh University Business Intelligence interview is considered moderately challenging, with a strong emphasis on practical analytics, data modeling, and communication skills. Candidates are evaluated on their ability to analyze complex institutional data, design robust data pipelines, and clearly present actionable insights to both technical and non-technical stakeholders. Familiarity with higher education data and experience in cross-functional collaboration will give you a distinct advantage.

5.2 How many interview rounds does Lehigh University have for Business Intelligence?
Typically, the interview process consists of 5–6 rounds. These include an application and resume review, recruiter screen, technical/case/skills round, behavioral interview, and final onsite or virtual interviews with various stakeholders. The process is comprehensive, ensuring a holistic assessment of both technical expertise and cultural fit.

5.3 Does Lehigh University ask for take-home assignments for Business Intelligence?
While not always required, Lehigh University may include a take-home technical or case assignment as part of the process. These assignments often focus on data analysis, dashboard design, or solving a real-world business problem relevant to the university’s operations. The goal is to assess your problem-solving skills, technical proficiency, and ability to communicate findings effectively.

5.4 What skills are required for the Lehigh University Business Intelligence?
Key skills include advanced SQL, data modeling, ETL pipeline design, and experience with data visualization tools. Strong analytical thinking, data cleaning and integration expertise, and the ability to translate complex data into actionable recommendations are essential. Communication skills are critical, as you’ll need to explain technical concepts to diverse audiences and drive data-informed decision-making across academic and administrative teams.

5.5 How long does the Lehigh University Business Intelligence hiring process take?
The typical hiring process spans 3–5 weeks from application to offer. Timelines may vary depending on candidate availability and stakeholder schedules, but most candidates can expect about a week between each stage, with technical and onsite interviews occasionally scheduled over several days.

5.6 What types of questions are asked in the Lehigh University Business Intelligence interview?
Expect a blend of technical and behavioral questions. Technical questions cover areas such as SQL queries, data pipeline design, dashboard creation, experiment measurement, and data cleaning. Case studies and scenario-based questions are common, focusing on real-world challenges faced by the university. Behavioral questions explore your experience collaborating across departments, communicating insights, handling ambiguity, and influencing decision-makers.

5.7 Does Lehigh University give feedback after the Business Intelligence interview?
Lehigh University typically provides feedback through the recruiting team, especially if you reach the final interview stages. While detailed technical feedback may be limited, you can expect high-level insights about your strengths and areas for improvement.

5.8 What is the acceptance rate for Lehigh University Business Intelligence applicants?
While specific acceptance rates are not published, the process is competitive given the university’s reputation and the strategic importance of business intelligence roles. Candidates with a strong mix of technical, analytical, and communication skills who demonstrate a clear understanding of higher education challenges have the best chance of success.

5.9 Does Lehigh University hire remote Business Intelligence positions?
Lehigh University has adapted to flexible work arrangements and may offer remote or hybrid options for Business Intelligence roles, depending on departmental needs and project requirements. Some positions may require occasional on-campus presence for key meetings or presentations, so it’s best to clarify expectations with the recruiter during the process.

• Related guides:
  • Lehigh University interview questions
  • Business Intelligence interview guide
  • Top Business Intelligence interview tips
  • Top 12 Business Intelligence Case Studies (Updated in 2025)
  • How to Prepare for Business Intelligence Interviews: Success Story

Ready to ace your Lehigh University Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like a Lehigh University Business Intelligence professional, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Lehigh University and similar institutions.

With resources like the Lehigh University Business Intelligence Interview Guide and our latest Business Intelligence case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition. You’ll find targeted practice in data analytics, SQL, dashboard design, experiment measurement, and communication—everything you need to succeed in a higher education environment.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!