Move Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Move? The Move Business Intelligence interview process typically spans a wide range of question topics and evaluates skills in areas like data pipeline design, data warehousing, dashboard development, stakeholder communication, and advanced analytics. Interview prep is especially important for this role at Move, as candidates are expected to demonstrate not only technical proficiency in managing and analyzing large, complex datasets but also the ability to translate data into actionable business insights that drive decision-making in a fast-paced, digital-first environment.

In preparing for the interview, you should:

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

Move is a leading provider of real estate information, tools, and services, best known for operating Realtor.com. The company connects home buyers, sellers, and real estate professionals through innovative technology platforms and comprehensive property listings. Move’s mission is to empower people by making all things home simple, efficient, and enjoyable. As a Business Intelligence professional at Move, you will play a crucial role in leveraging data-driven insights to optimize business operations and enhance the user experience across the company’s digital real estate platforms.

As a Business Intelligence professional at Move, you will be responsible for gathering, analyzing, and interpreting data to support strategic decision-making across the organization. You will work closely with various departments to develop dashboards, generate reports, and uncover actionable insights that drive business growth and operational efficiency. Typical tasks include identifying trends, measuring performance metrics, and presenting findings to leadership teams to inform product, marketing, and sales strategies. This role is key to helping Move leverage its data assets to enhance customer experiences and achieve its business objectives.

2.1 Stage 1: Application & Resume Review

This initial step is conducted by the recruiting team or business intelligence hiring manager. The focus is on evaluating your experience in data analytics, data warehousing, dashboard design, ETL pipeline development, and communication of actionable insights. The team looks for proficiency in SQL, Python, and experience with large-scale data systems, as well as evidence of stakeholder collaboration and problem-solving in complex environments. To prepare, ensure your resume clearly highlights relevant technical skills, impactful projects, and your ability to translate data into business value.

2.2 Stage 2: Recruiter Screen

Typically a 30-minute phone or video call led by a recruiter, this round assesses your motivation for the role, interest in Move, and overall fit for the company culture. Expect to discuss your background, career trajectory, and what excites you about business intelligence. Preparation should include a concise pitch of your experience, knowledge of Move’s business model, and clear reasons for pursuing this opportunity.

2.3 Stage 3: Technical/Case/Skills Round

Usually conducted by a BI team member, analytics lead, or technical manager, this round dives into your technical capabilities and problem-solving approach. You may be asked to design data pipelines, build data warehouses for new business models, write SQL queries, or architect dashboards for executive stakeholders. Expect case studies on topics like retail analytics, ride-sharing promotions, ETL troubleshooting, and data cleaning challenges. Preparation involves reviewing your technical foundations, practicing system design, and demonstrating your ability to handle messy, large-scale datasets and synthesize actionable insights.

2.4 Stage 4: Behavioral Interview

Led by a hiring manager or cross-functional partner, this interview explores your approach to teamwork, communication, and stakeholder management. You’ll be asked about past projects, how you presented complex insights to non-technical audiences, resolved misaligned expectations, and overcame challenges in data projects. Prepare by reflecting on examples where you influenced decision-making, ensured data quality, and navigated ambiguity or cross-cultural collaboration.

2.5 Stage 5: Final/Onsite Round

The final stage typically consists of multiple back-to-back interviews with BI team members, product leaders, and possibly executives. You may be asked to present a data-driven solution, walk through end-to-end pipeline design, and discuss trade-offs in system architecture or dashboard visualization. Expect deeper questions on business impact, cross-team collaboration, and your strategy for scaling analytics solutions. Preparation should focus on articulating your thought process, business acumen, and adaptability in fast-paced environments.

2.6 Stage 6: Offer & Negotiation

After successful completion of the previous rounds, the recruiter will reach out to discuss the offer package, compensation, benefits, and onboarding timeline. This is your opportunity to clarify any questions about the role, team structure, and growth opportunities. Preparation should include market research on compensation benchmarks and a clear understanding of your priorities.

2.7 Average Timeline

The Move Business Intelligence interview process typically spans 3-5 weeks from initial application to final offer. Fast-track candidates—those with highly relevant experience in data architecture, analytics, and stakeholder engagement—may progress through the stages in as little as 2-3 weeks, while the standard pace allows for about a week between each round to accommodate scheduling and team availability. Onsite or final rounds may be consolidated into a single day or spread over several days depending on interviewer schedules.

Next, let’s explore the types of interview questions you can expect at each stage of the Move Business Intelligence process.

3.1 Data Modeling & System Design

Business Intelligence at Move often requires designing robust data systems and pipelines that scale with business needs. You’ll be expected to architect solutions that support analytics, reporting, and decision-making across diverse domains. Focus on demonstrating your ability to create maintainable, scalable, and efficient data infrastructure.

3.1.1 Design a data warehouse for a new online retailer
Describe your approach to schema design, data integration, and scalability. Discuss how you would structure fact and dimension tables, support analytics needs, and ensure data consistency.

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes
Explain how you would ingest, clean, transform, and store data to support predictive analytics. Highlight key technologies, error handling, and monitoring strategies.

3.1.3 Design a system to synchronize two continuously updated, schema-different hotel inventory databases at Agoda
Discuss strategies for real-time data sync, schema reconciliation, and conflict resolution. Address scalability, latency, and data integrity concerns.

3.1.4 Design a dashboard that provides personalized insights, sales forecasts, and inventory recommendations for shop owners based on their transaction history, seasonal trends, and customer behavior
Outline how you would aggregate data, select key metrics, and build actionable visualizations. Mention personalization, predictive modeling, and user interface design.

3.1.5 Design a data pipeline for hourly user analytics
Describe the ETL process, aggregation logic, and data storage choices. Show how you’d optimize for both speed and accuracy in reporting.

3.2 Data Analysis & Insights

Move expects you to deliver actionable insights from complex datasets and communicate results clearly. These questions test your ability to analyze business scenarios, select appropriate metrics, and translate data into strategy.

3.2.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss tailoring your presentation style and level of detail to stakeholder needs, using visualizations and storytelling.

3.2.2 Making data-driven insights actionable for those without technical expertise
Explain how you simplify technical results and use analogies or visuals to ensure understanding.

3.2.3 Ensuring data quality within a complex ETL setup
Describe your approach to monitoring, validation, and troubleshooting within large-scale ETL environments.

3.2.4 Which metrics and visualizations would you prioritize for a CEO-facing dashboard during a major rider acquisition campaign?
Justify metric selection based on business impact and discuss visualization techniques for executive audiences.

3.2.5 User Experience Percentage
Show how you would calculate, interpret, and present user experience metrics to drive product improvements.

3.3 Experimentation & Statistical Analysis

Business Intelligence at Move relies on rigorous experimentation and statistical validation to support decision-making. Be ready to discuss how you design, analyze, and interpret experiments in a business context.

3.3.1 The role of A/B testing in measuring the success rate of an analytics experiment
Explain how you design experiments, select metrics, and use statistical tests to evaluate results.

3.3.2 An A/B test is being conducted to determine which version of a payment processing page leads to higher conversion rates. You’re responsible for analyzing the results. How would you set up and analyze this A/B test? Additionally, how would you use bootstrap sampling to calculate the confidence intervals for the test results, ensuring your conclusions are statistically valid?
Detail your approach to experiment setup, metric calculation, and confidence interval estimation using bootstrapping.

3.3.3 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?
Discuss experiment design, key metrics (e.g., retention, revenue impact), and analysis of results.

3.3.4 Say you work for Instagram and are experimenting with a feature change for Instagram stories.
Describe your approach to measuring feature impact, segmenting users, and interpreting experiment outcomes.

3.3.5 Assessing the market potential and then use A/B testing to measure its effectiveness against user behavior
Explain the process for combining market analysis with controlled experiments to validate product changes.

3.4 Data Engineering & Cleaning

Move values candidates who can handle large, messy datasets and automate data quality processes. You'll be expected to demonstrate your ability to clean, organize, and optimize data for analytics.

3.4.1 Describing a real-world data cleaning and organization project
Share your process for profiling, cleaning, and documenting data preparation steps.

3.4.2 Write a query to get the current salary for each employee after an ETL error.
Explain your approach to identifying and correcting data inconsistencies after pipeline failures.

3.4.3 Write a query to compute the average time it takes for each user to respond to the previous system message
Discuss how you would use window functions and time calculations to analyze user behavior.

3.4.4 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Describe your troubleshooting workflow, error logging, and strategies for process improvement.

3.4.5 You're analyzing political survey data to understand how to help a particular candidate whose campaign team you are on. What kind of insights could you draw from this dataset?
Discuss your approach to extracting actionable insights from surveys with complex response structures.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Focus on a specific instance where your analysis impacted a business outcome. Describe the problem, the data you used, and the measurable result.

3.5.2 Describe a challenging data project and how you handled it.
Highlight the complexity, obstacles you faced, and the steps you took to overcome them. Emphasize collaboration, resourcefulness, and impact.

3.5.3 How do you handle unclear requirements or ambiguity?
Explain your strategies for clarifying goals, asking the right questions, and iterating quickly with stakeholders to define scope.

3.5.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Share how you facilitated discussion, presented evidence, and found common ground to move the project forward.

3.5.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Describe your approach to simplifying complex concepts, using visual aids, and adapting your communication style.

3.5.6 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?
Discuss how you quantified the impact, re-prioritized tasks, and maintained transparency to protect project timelines and data quality.

3.5.7 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Explain your decision-making process, trade-offs made, and how you ensured future maintainability.

3.5.8 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Showcase your ability to facilitate consensus, document standards, and align metrics with business objectives.

3.5.9 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe how you built credibility, presented compelling evidence, and navigated organizational dynamics.

3.5.10 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Detail your approach to handling missing data, communicating uncertainty, and ensuring actionable recommendations.

4.1 Company-specific tips:

Demonstrate a deep understanding of Move’s mission and core products, especially Realtor.com and other digital real estate platforms. Familiarize yourself with how Move empowers home buyers, sellers, and real estate professionals through technology and data-driven solutions. Be prepared to discuss recent trends in the real estate industry, such as digital transformation, consumer behavior shifts, and the increasing role of data in optimizing user experiences and business operations.

Highlight your ability to translate complex analytics into actionable business strategies that align with Move’s focus on operational efficiency and user-centric innovation. Show that you can bridge the gap between technical data work and meaningful impact on business outcomes, especially in a fast-paced, digital-first environment.

Research Move’s approach to cross-functional collaboration and be ready to explain how you would partner with product, marketing, and sales teams to drive data-informed decisions. Understanding the importance of stakeholder management at Move is key—showcase your experience in tailoring communication to both technical and non-technical audiences, and your ability to influence decision-makers with clear, concise insights.

4.2 Role-specific tips:

Master the fundamentals of data pipeline design and data warehousing. Be ready to walk through how you would architect scalable, maintainable data pipelines and warehouses to support Move’s analytics needs. Practice explaining your choices around schema design, ETL processes, and data modeling, especially for large, complex datasets that are common in digital real estate platforms.

Sharpen your dashboard development and data visualization skills. Prepare to discuss how you would design executive dashboards that prioritize business-critical metrics, such as user engagement, conversion rates, or inventory trends. Focus on your ability to select the right visualizations for different audiences, and your experience personalizing dashboards to deliver actionable insights to stakeholders at all levels.

Refine your expertise in advanced analytics and experimentation. Expect questions on designing and analyzing A/B tests, measuring the impact of new features, and interpreting statistical results in a business context. Practice explaining how you would set up experiments, select and justify key metrics, and use statistical techniques like bootstrapping to ensure your conclusions are robust and actionable.

Showcase your approach to data cleaning and quality assurance. Be prepared with real-world examples of how you have profiled, cleaned, and organized messy or incomplete datasets. Explain the steps you take to ensure data integrity, monitor ETL processes, and troubleshoot pipeline failures—highlighting your attention to detail and commitment to delivering reliable analytics.

Demonstrate strong stakeholder communication and influence. Have stories ready where you successfully presented complex findings to non-technical audiences, resolved misaligned expectations, or influenced decisions without formal authority. Emphasize your ability to simplify technical concepts, use storytelling and visuals, and adapt your communication style to different stakeholders.

Prepare for behavioral questions that probe your collaboration and problem-solving skills. Reflect on past projects where you balanced short-term business needs with long-term data integrity, negotiated scope with multiple teams, or facilitated consensus on KPI definitions. Be ready to articulate your decision-making process, trade-offs considered, and the impact of your work on business objectives.

Practice articulating the business impact of your work. Move values candidates who can connect technical solutions to measurable business outcomes. Be ready to quantify the results of your analysis, such as increased revenue, improved user satisfaction, or enhanced operational efficiency, and explain how your insights drove strategic decisions.

By focusing your preparation on these company-specific and role-specific areas, you’ll be well-equipped to demonstrate the technical expertise, business acumen, and collaborative mindset that Move seeks in its Business Intelligence professionals.

5.1 How hard is the Move Business Intelligence interview?
The Move Business Intelligence interview is considered challenging but fair, with a strong emphasis on both technical and business acumen. Candidates are expected to demonstrate advanced skills in data pipeline design, dashboard development, and statistical analysis, alongside the ability to translate complex data into actionable business insights. The interview also probes your stakeholder management and communication abilities, reflecting Move’s fast-paced, digital-first environment.

5.2 How many interview rounds does Move have for Business Intelligence?
Move typically conducts 5-6 interview rounds for Business Intelligence roles. These include the initial application and resume review, recruiter screen, technical/case/skills interview, behavioral interview, final onsite (which may consist of multiple back-to-back interviews), and finally, the offer and negotiation stage.

5.3 Does Move ask for take-home assignments for Business Intelligence?
Yes, Move may ask candidates to complete a take-home assignment, usually focused on a real-world business analytics problem or dashboard design. This allows you to showcase your technical skills, analytical thinking, and ability to deliver clear, actionable insights in a format similar to what you’d encounter on the job.

5.4 What skills are required for the Move Business Intelligence?
Key skills for Move Business Intelligence professionals include advanced SQL and Python, data modeling, ETL pipeline development, dashboard and data visualization design, statistical analysis (including A/B testing), and strong communication with both technical and non-technical stakeholders. Experience with large-scale data systems and the ability to connect analytics to business strategy are also highly valued.

5.5 How long does the Move Business Intelligence hiring process take?
The typical timeline for the Move Business Intelligence hiring process is 3-5 weeks from initial application to final offer. Fast-track candidates with highly relevant experience may move through the stages in 2-3 weeks, while the standard pace allows about a week between each round to accommodate interviewer schedules.

5.6 What types of questions are asked in the Move Business Intelligence interview?
Expect a mix of technical and behavioral questions. Technical questions cover topics like data warehouse design, ETL troubleshooting, dashboard development, and statistical analysis (including experiment design and interpretation). Behavioral questions focus on stakeholder management, communication strategies, handling ambiguity, and examples of driving business impact with data.

5.7 Does Move give feedback after the Business Intelligence interview?
Move typically provides high-level feedback through recruiters, especially after onsite or final rounds. Detailed technical feedback may be limited, but you can expect to hear about your strengths and areas for improvement, which can help you prepare for future interviews.

5.8 What is the acceptance rate for Move Business Intelligence applicants?
While specific acceptance rates aren’t publicly available, Move’s Business Intelligence roles are competitive, with an estimated acceptance rate of 3-5% for qualified applicants. Candidates with strong technical backgrounds and clear business impact in their past work have a distinct advantage.

5.9 Does Move hire remote Business Intelligence positions?
Yes, Move offers remote opportunities for Business Intelligence roles, with some positions requiring occasional office visits for team collaboration and strategic meetings. The company supports flexible work arrangements to attract top talent from diverse locations.

• Related guides:
  • Move interview questions
  • Business Intelligence interview guide
  • Top Business Intelligence interview tips

Ready to ace your Move Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like a Move 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 Move and similar companies.

With resources like the Move Business Intelligence Interview Guide and our latest 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.

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!