At Hopper, the Business Analyst role is a highly analytical, impact-driven position that sits at the intersection of data science, product development, and business strategy. Hopper operates in a high-volume, real-time travel marketplace where minute changes in user behavior, pricing algorithms, and conversion funnels directly affect the company's bottom line. As a Business Analyst, you are not just compiling reports; you are translating massive, complex datasets into strategic recommendations that shape the future of Hopper’s proprietary fintech products (such as Price Freeze, Cancel for Any Reason, and Flight Disruption Guarantee).

The impact of this role is immediate and measurable. You will collaborate closely with product managers, engineers, and operations teams to optimize pricing strategies, identify friction points in the booking funnel, and evaluate the launch of new features. Because Hopper operates as a merchant of record for many of its transactions, your analytical models will directly influence risk management, margin optimization, and customer retention strategies.

To succeed in this role, you must possess a strong quantitative toolkit, a product-centric mindset, and the ability to thrive in a fast-paced, highly autonomous environment. The team values analysts who do not wait for explicit instructions but instead proactively dive into unstructured data to uncover hidden patterns and business opportunities.

To help you prepare, we have categorized representative questions based on real reported interview experiences at Hopper. These questions illustrate the core themes you are likely to face, focusing on analytical depth, business acumen, and structural problem-solving.

Data Interpretation & Insight Generation

These questions evaluate your ability to handle highly ambiguous data tasks and extract actionable business value from raw datasets.

• Given a dataset containing flight search and booking history, how would you identify the top three friction points causing users to drop off before purchasing?
• If you are handed a broad, unstructured dataset with no specific prompt other than "find valuable insights," what systematic framework would you use to narrow your focus?

Preparing for an interview at Hopper requires a balanced approach of technical preparation, business case practice, and mental readiness for an intense, fast-paced evaluation process.

Data Fluency & Insight Generation – Hopper heavily evaluates your ability to work with raw, unstructured data. You must demonstrate that you can take a broad dataset, formulate clean hypotheses, clean and analyze the data, and extract high-impact business insights without needing hand-holding.

Structured Problem-Solving – When presented with business case studies, your interviewers will look at how you structure your thinking. You should clearly define the business objective, outline your analytical framework, explain your assumptions, and tie your final recommendation back to Hopper’s core business metrics.

Communication & Stakeholder Management – You will interact with diverse teams across the organization. You must show that you can defend your analytical methodologies under pressure, accept constructive feedback, and articulate your thoughts clearly and concisely, even when interrupted or challenged.

Adaptability & Resilience – The hiring process at Hopper can move very quickly and sometimes feel intense. Being able to adapt to different interviewer styles—ranging from highly collaborative to direct and fast-paced—is crucial to demonstrating your cultural fit.

The interview process for a Business Analyst at Hopper is designed to thoroughly test both your technical capabilities and your business intuition. The process typically begins with a standard recruiter or hiring manager screening call. This initial conversation is relatively casual but highly insightful, focusing on your background, your interest in Hopper, and a high-level overview of the role’s responsibilities.

Following a successful screen, candidates enter the technical assessment phase. This often involves a multi-stage take-home data exercise. In some variations of the process, you may receive an initial screening data exercise, followed by a more complex, take-home data project with a flexible timeline (typically 1 to 2 weeks). Once submitted, you will participate in a technical walkthrough interview—often via video call—with the hiring manager or a senior analyst to present your findings, walk through your code or models, and defend your analytical decisions.

For some teams, the final stage can involve a comprehensive panel interview process. This can consist of up to six rounds with individuals across different areas of the department. These rounds cover various case studies, behavioral assessments, and cross-functional collaboration scenarios to ensure you can work effectively with different stakeholders.

The visual timeline above outlines the typical progression from application to final decision. While some pipelines are streamlined with quick turnarounds, other specialized teams may require a more extensive multi-round panel. You should use this timeline to pace your preparation, ensuring your technical take-home is polished before moving to the live case presentation stages.

To excel in the Hopper interview process, you must understand the specific areas where you will be evaluated and how to demonstrate mastery in each.

Data Take-Home & Walkthrough

The take-home assignment is a critical filter in the Hopper hiring process. It simulates the actual day-to-day work of a Business Analyst by providing you with a representative dataset and testing how you self-direct your analysis.

Be ready to go over:

• Hypothesis formulation – How you define the business questions you want to answer before querying the data.
• Data cleaning and preparation – Your ability to handle missing values, duplicates, and unstructured formats efficiently.
• Insight extraction – Moving beyond basic descriptive statistics to identify actual trends, anomalies, and business opportunities.

Example scenarios:

• "You are given a dataset of flight search queries and subsequent purchases. Identify which user segments have the highest price sensitivity and suggest a targeted pricing strategy."
• "Analyze a raw dataset of app clickstream data and present a clean, structured walkthrough of the user journey, highlighting where the greatest drop-offs occur."

Product & Business Case Studies

During live technical interviews, you will face case studies that test your business acumen and your understanding of travel fintech.

Be ready to go over:

• Funnel optimization – Analyzing conversion rates, click-through rates, and user retention metrics.
• Fintech risk and pricing – Understanding how Hopper structures products like Price Freeze and how to balance consumer demand with financial risk.
• A/B testing methodology – Designing clean experiments, defining primary and secondary metrics, and determining statistical significance.
• Advanced concepts (less common) – Multi-touch attribution modeling, customer lifetime value (LTV) forecasting, and dynamic pricing elasticity.

Example scenarios:

• "How would you determine the optimal fee to charge for our Cancel for Any Reason product on a high-demand flight route?"
• "If we want to launch a new subscription service for frequent travelers, how would you estimate the target pricing and project the impact on our existing transactional revenue?"

Collaborative & Panel Interviews

The final rounds will evaluate how you function within a cross-functional team and how you handle the interpersonal dynamics of a fast-growing tech company.

Be ready to go over:

• Stakeholder alignment – How you build consensus when product, engineering, and finance have competing priorities.
• Handling critical feedback – Your response when an interviewer challenges your mathematical assumptions or data interpretations.
• Communication clarity – Keeping your answers structured, even when interviewers ask rapid-follow-up questions or interrupt your flow.

Example scenarios:

• "Describe a time when your analysis disproved a favorite hypothesis of a senior leader. How did you present the news?"
• "How do you manage your workflow when engineering requires immediate metric definitions, but finance is demanding a quarterly revenue forecast?"