Move Data Analyst Interview Guide

Getting ready for a Data Analyst interview at Move? The Move Data Analyst interview process typically spans a broad range of question topics and evaluates skills in areas like SQL and Python analytics, data pipeline design, stakeholder communication, and experiment analysis. Interview preparation is especially important for this role at Move, as Data Analysts are expected to deliver actionable insights from complex datasets, communicate findings clearly to diverse audiences, and drive data-driven decision-making in a fast-paced, consumer-focused environment.

In preparing for the interview, you should:

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

Move is a technology-driven company focused on revolutionizing the real estate industry by providing digital solutions that simplify the process of buying, selling, and renting homes. As the operator of leading platforms such as Realtor.com, Move connects millions of consumers with trusted real estate professionals and up-to-date property listings. The company leverages data and technology to empower users with actionable insights and a seamless property search experience. As a Data Analyst, you will contribute to Move’s mission by analyzing data to drive business decisions and enhance user engagement across its digital platforms.

As a Data Analyst at Move, you will be responsible for gathering, processing, and analyzing data to support business decisions and strategic initiatives within the real estate technology sector. You will collaborate with cross-functional teams such as product, marketing, and engineering to identify trends, create reports, and develop actionable insights that enhance user experience and drive company growth. Key tasks include building dashboards, conducting data quality checks, and presenting findings to stakeholders. This role is crucial in helping Move leverage data to optimize operations and achieve its mission of connecting people with real estate solutions more effectively.

2.1 Stage 1: Application & Resume Review

The initial stage at Move for Data Analyst candidates involves a detailed review of your resume and application materials, typically conducted by the recruiting team or an HR coordinator. This review emphasizes your experience with data analysis, proficiency in SQL and Python, familiarity with data cleaning and pipeline design, and your ability to present actionable insights to non-technical stakeholders. Make sure your resume clearly highlights real-world data projects, experience with large datasets, and examples of stakeholder communication.

2.2 Stage 2: Recruiter Screen

The recruiter screen is usually a 30-minute phone or video call with a recruiter or HR representative. Expect questions about your motivation for applying to Move, your understanding of the company’s data-driven mission, and a high-level overview of your technical and analytical background. Prepare to articulate your interest in the company, discuss how your skills align with the role, and demonstrate your ability to communicate data concepts in an accessible way.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically consists of one or two interviews, conducted by members of the data team or analytics managers. Expect to be assessed on your technical skills through SQL and Python exercises, case studies involving experiment design and A/B testing, and questions about data pipeline architecture. You may be asked to analyze complex datasets, design solutions for data quality issues, and present clear, actionable insights. Preparation should include reviewing your experience with large-scale data manipulation, ETL processes, and your approach to cleaning and aggregating data from multiple sources.

2.4 Stage 4: Behavioral Interview

The behavioral interview is often led by a hiring manager or team lead and focuses on your problem-solving approach, communication skills, and adaptability in cross-functional settings. You’ll discuss past experiences managing stakeholder expectations, overcoming project hurdles, and making data accessible to non-technical users. Be ready to share examples of how you’ve resolved misaligned expectations, collaborated with diverse teams, and translated technical findings into strategic business recommendations.

2.5 Stage 5: Final/Onsite Round

The final or onsite round may involve a series of interviews with senior team members, including analytics directors, product managers, and cross-functional partners. This stage dives deeper into your ability to handle business-critical data challenges, present insights to executive audiences, and design scalable solutions for data infrastructure. You may be asked to walk through the lifecycle of a data project, respond to scenario-based questions about user journey analysis or dashboard design, and demonstrate your strategic thinking in ambiguous situations.

2.6 Stage 6: Offer & Negotiation

If you progress through all interview stages, the recruiter will reach out to discuss the offer details, including compensation, start date, and team placement. This is your opportunity to negotiate terms and clarify any remaining questions about the role or company culture.

2.7 Average Timeline

The Move Data Analyst interview process typically spans 3-4 weeks from initial application to offer, with some fast-track candidates completing the process in as little as 2 weeks. The standard pace involves about a week between each stage, while scheduling for onsite interviews may vary based on team availability and candidate preferences.

Now, let’s explore the specific types of interview questions that Move Data Analyst candidates have encountered in this process.

3.1. Data Cleaning & Quality

Data analysts at Move frequently encounter messy, incomplete, or inconsistent datasets from various sources. You’ll be expected to demonstrate how you systematically clean, validate, and organize data to ensure reliable analysis and reporting. Focus on your approach to profiling data, handling missing values, and communicating the impact of data quality to stakeholders.

3.1.1 Describing a real-world data cleaning and organization project
Summarize the initial state of the data, key challenges (e.g., duplicates, nulls, formatting), and your step-by-step cleaning process. Highlight the tools and techniques you used and how your work enabled better business decisions.

3.1.2 How would you approach improving the quality of airline data?
Outline your process for identifying and prioritizing data quality issues, including validation, anomaly detection, and root cause analysis. Discuss how you’d implement fixes and monitor ongoing quality.

3.1.3 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Describe your troubleshooting workflow, including logging, error categorization, and root cause analysis. Emphasize proactive monitoring and communication with engineering or IT teams.

3.1.4 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Discuss how you’d profile and restructure a dataset with inconsistent layouts, and propose practical steps for standardization and error prevention.

3.2. Data Modeling & Pipeline Design

Move’s data analysts design and optimize pipelines that aggregate, transform, and deliver data for reporting and analytics. You’ll need to show your ability to architect scalable solutions and select appropriate tools for the job.

3.2.1 Design a data pipeline for hourly user analytics.
Explain how you’d architect an end-to-end pipeline, including data ingestion, transformation, aggregation, and storage. Mention scalability and reliability considerations.

3.2.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Describe the stages of the pipeline, from raw data collection to feature engineering and serving predictions. Highlight your approach to monitoring and error handling.

3.2.3 Design a data warehouse for a new online retailer
Lay out your schema design, data modeling principles, and ETL strategies. Discuss how you’d ensure scalability and flexibility for future analytics needs.

3.2.4 Let's say that you're in charge of getting payment data into your internal data warehouse.
Walk through your pipeline design for ingesting, validating, and transforming payment data, focusing on data integrity and timeliness.

3.3. SQL & Analytical Techniques

Strong SQL and analytical skills are essential for Move data analysts to extract insights and answer complex business questions. Expect to demonstrate your ability to write efficient queries and apply advanced techniques to real-world scenarios.

3.3.1 Write a query to calculate the 3-day weighted moving average of product sales.
Describe how you’d use window functions, partitioning, and weighting logic to compute moving averages over time.

3.3.2 Write a query to calculate the conversion rate for each trial experiment variant
Explain how you’d aggregate trial data, count conversions, and handle missing or ambiguous entries.

3.3.3 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?
Outline your approach to data integration, including joining, cleaning, and reconciling discrepancies, followed by your strategy for extracting actionable insights.

3.3.4 python-vs-sql
Discuss scenarios where you’d choose Python over SQL (or vice versa) for data manipulation, and justify your decision based on scalability, complexity, and maintainability.

3.4. Experimentation & Metrics

Move relies on experimentation and rigorous metric analysis to drive product and business decisions. You’ll be expected to design tests, select appropriate KPIs, and interpret results to inform strategy.

3.4.1 What kind of analysis would you conduct to recommend changes to the UI?
Describe how you’d use user journey and engagement data to identify pain points and propose UI improvements, referencing metrics and visualization techniques.

3.4.2 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?
Lay out an experimental design, key metrics to monitor (e.g., retention, revenue, churn), and how you’d interpret results to guide business decisions.

3.4.3 The role of A/B testing in measuring the success rate of an analytics experiment
Explain your approach to designing experiments, selecting control and treatment groups, and measuring outcomes.

3.4.4 We're interested in determining if a data scientist who switches jobs more often ends up getting promoted to a manager role faster than a data scientist that stays at one job for longer.
Describe your analytical strategy, including cohort analysis and regression modeling, to test the hypothesis and interpret findings.

3.5. Communication & Stakeholder Management

Effective communication and stakeholder alignment are critical for Move data analysts to ensure insights are understood and acted upon. You’ll need to demonstrate your ability to tailor presentations, resolve misalignments, and make data accessible to all audiences.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Share your approach for distilling complex findings into actionable recommendations, using visualization and storytelling.

3.5.2 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Describe how you navigate conflicting priorities and communicate trade-offs to reach consensus.

3.5.3 Making data-driven insights actionable for those without technical expertise
Explain how you bridge the gap between technical analysis and business decision-making for non-technical stakeholders.

3.5.4 Demystifying data for non-technical users through visualization and clear communication
Discuss your strategies for designing intuitive dashboards and reports that empower stakeholders to self-serve insights.

3.6 Behavioral Questions

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

3.6.2 Describe a challenging data project and how you handled it.
Outline the obstacles you faced, your problem-solving approach, and how you ensured a successful result despite setbacks.

3.6.3 How do you handle unclear requirements or ambiguity?
Discuss how you clarify objectives, communicate with stakeholders, and iterate on deliverables when requirements are not well-defined.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Describe the communication barriers, strategies you used to bridge gaps, and the outcome of your efforts.

3.6.5 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Explain your approach to persuasion, using evidence and empathy to build buy-in.

3.6.6 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?
Share how you managed competing priorities, communicated trade-offs, and protected project timelines and data integrity.

3.6.7 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Discuss your decision-making process, including what compromises you made and how you safeguarded future data quality.

3.6.8 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Outline how you discovered the mistake, communicated it transparently, and what steps you took to remediate and prevent recurrence.

3.6.9 Describe a time you proactively identified a business opportunity through data.
Share how you spotted an insight, pitched it to stakeholders, and drove a positive business change.

3.6.10 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Explain your prioritization framework, tools you use to stay organized, and strategies for managing competing demands.

4.1 Company-specific tips:

Get familiar with Move’s mission to streamline real estate transactions through data-driven technology. Understand how platforms like Realtor.com use analytics to connect consumers with properties and professionals, and research how Move leverages data to enhance user experience and drive business decisions in the real estate sector.

Review recent product launches and data initiatives at Move, such as new search features, personalization strategies, or partnerships with real estate agents. Demonstrating knowledge of these efforts shows your genuine interest and ability to connect your analytical skills with the company’s evolving needs.

Explore Move’s consumer-facing products and think about the types of data generated—user journeys, listing interactions, conversion funnels, and engagement metrics. Prepare to discuss how you might use such data to uncover actionable insights that improve product performance or customer satisfaction.

4.2 Role-specific tips:

4.2.1 Practice structuring answers around data cleaning and quality improvement.
Be ready to walk through real examples of cleaning messy datasets, handling missing values, and validating data sources. Explain your systematic approach to resolving data quality issues and highlight how your work enables more reliable analysis and reporting for business stakeholders.

4.2.2 Demonstrate your data pipeline design skills with practical scenarios.
Prepare to architect end-to-end data pipelines, including ingestion, transformation, aggregation, and storage. Discuss how you ensure scalability, reliability, and error handling—especially in fast-paced environments like Move where real-time analytics can drive consumer engagement.

4.2.3 Show mastery in writing efficient SQL queries and applying analytical techniques.
Practice explaining your logic for complex queries, such as calculating moving averages, conversion rates, or integrating multiple data sources. Be ready to justify when you’d use SQL versus Python for different data manipulation tasks, focusing on scalability and maintainability.

4.2.4 Highlight your experience with experimentation and metric-driven decision-making.
Describe your approach to designing A/B tests, selecting key performance indicators, and interpreting experiment results. Use examples from past roles to show how your insights have influenced product or business strategy, especially in consumer-facing environments.

4.2.5 Refine your ability to communicate insights to diverse audiences.
Practice distilling complex analyses into clear, actionable recommendations for both technical and non-technical stakeholders. Prepare to discuss how you use visualization, storytelling, and tailored presentations to make data accessible and drive alignment across teams.

4.2.6 Prepare stories that showcase your stakeholder management and cross-functional collaboration.
Think of examples where you navigated misaligned expectations, resolved communication barriers, or influenced decisions without formal authority. Emphasize your strategies for building consensus and ensuring that data-driven recommendations are understood and acted upon.

4.2.7 Be ready to discuss behavioral scenarios involving ambiguity, prioritization, and error management.
Reflect on times you managed unclear requirements, balanced multiple deadlines, or caught mistakes in your analysis after sharing results. Prepare to walk through your problem-solving process, how you communicate transparently, and the steps you take to remediate and prevent future issues.

4.2.8 Illustrate your proactive approach to identifying business opportunities through data.
Share examples of how you’ve spotted trends or insights in data that led to positive change for your organization. Emphasize your initiative, ability to pitch ideas to stakeholders, and the measurable impact of your recommendations.

4.2.9 Show your commitment to long-term data integrity, even under pressure.
Discuss how you balance the need for quick wins—such as shipping dashboards fast—with the importance of maintaining data quality and reliability for future analysis. Explain your decision-making framework and how you safeguard business-critical data in high-stakes environments.

5.1 How hard is the Move Data Analyst interview?
The Move Data Analyst interview is challenging but fair, designed to assess both technical expertise and business acumen. You’ll be tested on real-world data cleaning, SQL and Python analytics, pipeline design, experiment analysis, and stakeholder communication. Candidates who prepare for both technical and behavioral scenarios, and who can connect their skills to Move’s mission of revolutionizing real estate, have a distinct advantage.

5.2 How many interview rounds does Move have for Data Analyst?
Typically, there are 5-6 rounds in the Move Data Analyst interview process. These include an initial application and resume review, recruiter screen, technical/case/skills interviews, behavioral interviews, a final onsite or virtual round with senior team members, and the offer/negotiation stage.

5.3 Does Move ask for take-home assignments for Data Analyst?
While take-home assignments are not guaranteed, Move may include a practical exercise or case study in the technical round. This could involve cleaning a dataset, designing a pipeline, or analyzing metrics relevant to real estate platforms, allowing you to showcase your analytical approach and communication skills.

5.4 What skills are required for the Move Data Analyst?
Essential skills for a Move Data Analyst include advanced SQL, Python for data analysis, data pipeline design, experiment analysis (like A/B testing), and strong stakeholder communication. Experience with data cleaning, dashboard creation, and translating complex insights for non-technical audiences is highly valued.

5.5 How long does the Move Data Analyst hiring process take?
The typical timeline is 3-4 weeks from application to offer, though some candidates may complete the process in as little as 2 weeks. Delays can occur based on team availability and scheduling preferences for onsite interviews.

5.6 What types of questions are asked in the Move Data Analyst interview?
Expect a mix of technical and behavioral questions: SQL and Python exercises, data pipeline and modeling scenarios, experiment design, metric analysis, and case studies relevant to real estate analytics. Behavioral questions will probe your problem-solving, stakeholder management, and ability to communicate data-driven insights.

5.7 Does Move give feedback after the Data Analyst interview?
Move typically provides feedback through recruiters, focusing on your strengths and areas for improvement. While detailed technical feedback may be limited, you can expect constructive input on your interview performance and fit for the role.

5.8 What is the acceptance rate for Move Data Analyst applicants?
While exact figures aren’t public, the Move Data Analyst role is competitive, with an estimated acceptance rate of 3-6% for qualified applicants. Demonstrating strong technical skills and clear alignment with Move’s mission will help you stand out.

5.9 Does Move hire remote Data Analyst positions?
Yes, Move offers remote opportunities for Data Analysts, with some roles requiring occasional in-office collaboration. Flexibility varies by team, but Move embraces a hybrid work culture to attract top analytics talent across locations.

• Related guides:
  • Move interview questions
  • Data Analyst interview guide
  • Top data analyst interview tips

Ready to Ace Your Interview?

Ready to ace your Move Data Analyst interview? It’s not just about knowing the technical skills—you need to think like a Move Data Analyst, 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 Data Analyst 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!