Move Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Move? The Move Data Engineer interview process typically spans a wide range of question topics and evaluates skills in areas like data pipeline design, ETL development, large-scale data processing, and effective communication of technical concepts. Interview preparation is especially important for this role at Move, as Data Engineers are expected to architect robust data solutions that support the company’s digital platforms, ensure high-quality data flows, and collaborate with both technical and non-technical stakeholders in a dynamic, fast-paced environment.

In preparing for the interview, you should:

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

Move is a technology company specializing in real estate solutions, providing digital platforms that connect home buyers, sellers, and professionals. The company operates Realtor.com, a leading online real estate marketplace, offering data-driven insights and property listings to millions of users across the United States. Move’s mission is to simplify and optimize the home buying and selling process through trusted information and innovative technology. As a Data Engineer, you will contribute to building and maintaining scalable data infrastructure that supports Move’s commitment to delivering accurate, actionable real estate data to consumers and industry partners.

As a Data Engineer at Move, you are responsible for designing, building, and maintaining the data infrastructure that supports the company’s products and business operations. You will work closely with data scientists, analysts, and software engineers to ensure reliable data pipelines, optimize data storage, and enable efficient data processing. Core tasks include developing ETL processes, managing databases, and ensuring data quality and integrity. This role is critical in empowering teams across Move to access accurate, timely data for decision-making, contributing directly to the company’s ability to deliver innovative solutions in the real estate technology sector.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application and resume, focusing on your experience with building and maintaining data pipelines, data warehouse architecture, ETL systems, and large-scale data processing. Recruiters and hiring managers look for evidence of hands-on skills in Python, SQL, cloud data platforms, and experience in designing scalable and robust data solutions. To prepare, ensure your resume highlights specific data engineering projects, quantifiable impact, and technical toolsets relevant to the role.

2.2 Stage 2: Recruiter Screen

The recruiter screen is typically a 30-minute phone call where a recruiter assesses your motivation for joining Move, your understanding of the company’s data-driven products, and your alignment with the team’s culture. Expect to discuss your career trajectory, communication skills, and high-level technical background. Preparation should include a clear and concise narrative about your experience, why you are interested in Move, and how your skills fit the company’s data engineering needs.

2.3 Stage 3: Technical/Case/Skills Round

This stage often consists of one or more technical interviews—either live coding or take-home assignments—focused on data pipeline design, ETL troubleshooting, and large-scale data processing. Interviewers (such as senior data engineers or engineering managers) will evaluate your ability to design end-to-end pipelines, optimize SQL queries, and handle real-world data quality and transformation challenges. You may be asked to architect solutions for ingesting, cleaning, and aggregating data, as well as to demonstrate proficiency in Python and SQL. Preparation should focus on practicing system design for data warehousing, debugging ETL failures, and efficiently manipulating large datasets.

2.4 Stage 4: Behavioral Interview

The behavioral interview explores your collaboration style, stakeholder management, and adaptability in fast-paced environments. Panelists may include cross-functional partners such as data scientists, product managers, or analytics leads. You’ll be expected to demonstrate how you communicate complex data concepts to non-technical stakeholders, resolve misaligned expectations, and drive successful project outcomes. Prepare by reflecting on past experiences where you led data projects, navigated ambiguity, or made technical insights accessible to broader audiences.

2.5 Stage 5: Final/Onsite Round

The final round (often virtual onsite) typically involves a series of interviews with team members from data engineering, analytics, and product. This stage may include a mix of technical deep-dives, case studies, and scenario-based questions that assess both your technical expertise and your ability to collaborate cross-functionally. You may also be asked to present a previous data project, walk through your approach to solving a complex data challenge, or respond to hypothetical business problems requiring data-driven solutions. Preparation should include practicing technical presentations, reviewing end-to-end project experiences, and demonstrating a structured approach to ambiguous data problems.

2.6 Stage 6: Offer & Negotiation

Successful candidates will receive an offer from Move’s recruiting team, followed by discussions on compensation, benefits, and start date. This stage may involve conversations with HR or the hiring manager to finalize details and answer any remaining questions about the role or team expectations.

2.7 Average Timeline

The typical Move Data Engineer interview process spans 3-5 weeks from initial application to offer, with each stage generally taking about a week. Fast-track candidates with highly relevant experience or internal referrals may move through the process in as little as 2-3 weeks, while the standard pace allows for interview scheduling and take-home assignments. The technical/case round may include a 3-5 day window for completion, and onsite rounds are coordinated based on team availability.

Next, let’s break down the types of questions you can expect at each stage of the Move Data Engineer interview process.

3.1. Data Pipeline Design & ETL

Data pipeline and ETL (Extract, Transform, Load) design is central to the Data Engineer role at Move. Expect questions that assess your ability to architect, optimize, and troubleshoot scalable pipelines for diverse data sources and business needs.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Describe your approach to handling varying data formats, ensuring data quality, and providing fault tolerance. Discuss how you would automate ingestion and transformation while maintaining flexibility for new sources.

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Explain how you would architect a pipeline from raw data ingestion to serving predictions, including storage, processing, and monitoring. Emphasize modularity, scalability, and how you’d ensure data reliability.

3.1.3 Let's say that you're in charge of getting payment data into your internal data warehouse.
Detail the steps to extract, validate, transform, and load payment data, addressing issues like schema changes and late-arriving data. Discuss how you’d ensure data integrity and enable downstream analytics.

3.1.4 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Outline your approach to handling large file uploads, error handling, and incremental updates. Highlight best practices for schema validation, data deduplication, and efficient reporting.

3.1.5 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Describe your troubleshooting process, including logging, alerting, and root cause analysis. Discuss how you’d implement automated recovery and long-term solutions to prevent recurrence.

3.2. Data Modeling & Warehousing

Data modeling and warehouse design are essential skills for supporting analytics and business intelligence at Move. Be prepared to discuss designing schemas, optimizing storage, and supporting evolving business requirements.

3.2.1 Design a data warehouse for a new online retailer.
Explain your approach to schema design, handling slowly changing dimensions, and supporting both transactional and analytical queries. Discuss considerations for scalability and cost.

3.2.2 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Describe your selection of tools for ingestion, storage, transformation, and visualization. Emphasize cost-effectiveness, reliability, and extensibility.

3.2.3 Design a data pipeline for hourly user analytics.
Discuss how you would aggregate and store high-frequency event data, ensuring timely and accurate reporting. Include strategies for partitioning and indexing.

3.3. Data Quality & Cleaning

Ensuring data quality and performing effective cleaning are critical for reliable analytics and machine learning at Move. You will be evaluated on your ability to identify, diagnose, and resolve data integrity issues.

3.3.1 Describing a real-world data cleaning and organization project
Share a specific example where you tackled messy, inconsistent, or incomplete data. Explain your methodology for profiling, cleaning, and validating the dataset.

3.3.2 How would you approach improving the quality of airline data?
Detail your process for identifying quality issues, prioritizing fixes, and implementing automated checks. Discuss how you’d balance speed with thoroughness.

3.3.3 Ensuring data quality within a complex ETL setup
Describe techniques for monitoring, validating, and reconciling data across multiple ETL stages. Highlight your approach to root cause analysis and remediation.

3.3.4 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Explain how you would reformat and standardize irregular data layouts to enable robust analysis. Discuss common pitfalls and how to avoid them.

3.4. System Design & Scalability

Move values engineers who can design systems that are robust, scalable, and adaptable to changing business needs. You may be asked to architect solutions for new products or optimize existing ones for performance.

3.4.1 System design for a digital classroom service.
Walk through your approach to designing a scalable, secure, and maintainable digital classroom system. Address data storage, real-time updates, and user privacy.

3.4.2 Describing a data project and its challenges
Share a project where you overcame technical or organizational hurdles. Focus on your problem-solving skills and how you ensured successful delivery.

3.4.3 Modifying a billion rows
Discuss strategies for efficiently updating or transforming massive datasets. Include considerations for minimizing downtime, ensuring consistency, and handling failures.

3.5. Programming & Data Manipulation

Strong programming skills are required for Move Data Engineers, especially in Python and SQL. You may be asked to solve problems involving data transformation, aggregation, and custom logic.

3.5.1 python-vs-sql
Explain when you would choose Python over SQL (or vice versa) for various data engineering tasks. Discuss trade-offs in terms of scalability, maintainability, and performance.

3.5.2 Write a function that splits the data into two lists, one for training and one for testing.
Describe your logic for partitioning data without relying on high-level libraries. Consider edge cases and efficiency.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a project where your analysis led to a concrete business outcome. Describe the problem, your approach, and the impact of your recommendation.

3.6.2 Describe a challenging data project and how you handled it.
Share a specific example, emphasizing technical and interpersonal hurdles. Highlight your problem-solving process and the results achieved.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your strategy for clarifying goals, communicating with stakeholders, and iteratively refining deliverables.

3.6.4 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?
Describe how you fostered open communication, incorporated feedback, and achieved alignment.

3.6.5 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Discuss your process for reconciling differences, facilitating consensus, and documenting definitions.

3.6.6 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Highlight how you identified automation opportunities, implemented solutions, and measured improvements.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Emphasize your communication skills, use of evidence, and ability to build trust.

3.6.8 Describe a time you had to deliver an overnight report and still guarantee the numbers were “executive reliable.” How did you balance speed with data accuracy?
Explain your triage process, quality controls, and how you communicated uncertainty.

3.6.9 Share how you communicated unavoidable data caveats to senior leaders under severe time pressure without eroding trust.
Describe your approach to transparency, managing expectations, and maintaining credibility.

4.1 Company-specific tips:

Familiarize yourself with Move’s core business model, especially how Realtor.com leverages data to connect buyers, sellers, and real estate professionals. Understand the critical role data plays in powering property listings, search algorithms, and consumer insights. Research recent product launches or data-driven initiatives at Move, and be ready to discuss how data engineering supports these efforts.

Dive into the unique challenges of real estate data, such as integrating heterogeneous data sources (MLS feeds, partner APIs, user-generated content) and maintaining high standards of data accuracy and timeliness. Be prepared to articulate how robust pipelines can directly impact the user experience and business outcomes at Move.

Review Move’s commitment to innovation and trusted information. Prepare examples that showcase your ability to deliver reliable, scalable data solutions that align with the company’s mission to simplify the home buying and selling process.

4.2 Role-specific tips:

4.2.1 Practice designing scalable ETL pipelines for ingesting and transforming diverse real estate data.
Think through how you would architect end-to-end data pipelines that can handle multiple data formats (CSV, JSON, XML) from external partners and internal systems. Emphasize automation, schema evolution, and robust error handling in your designs. Be ready to discuss strategies for incremental updates, late-arriving data, and ensuring data integrity throughout the pipeline.

4.2.2 Demonstrate experience with data modeling and warehouse architecture tailored for analytics and reporting.
Prepare to explain your approach to designing data warehouses that support both transactional and analytical workloads. Discuss how you handle slowly changing dimensions, partitioning strategies, and indexing for high-frequency real estate event data. Show your ability to balance scalability, cost, and query performance.

4.2.3 Highlight your skills in data quality assurance and cleaning for complex, real-world datasets.
Share specific examples where you identified and resolved data inconsistencies, missing values, or schema mismatches. Describe your methodology for profiling, cleaning, and validating large datasets, and explain how you automated data quality checks to prevent recurring issues.

4.2.4 Prepare to discuss system design and scalability for data infrastructure supporting millions of listings and users.
Walk through your approach to architecting systems that are robust, maintainable, and capable of scaling with business growth. Address considerations for real-time updates, secure data storage, and efficient batch processing. Be ready to talk about handling billion-row updates and minimizing downtime.

4.2.5 Showcase your proficiency in Python and SQL for data manipulation and pipeline development.
Demonstrate your ability to choose the right tool for each task, whether it’s complex transformations in Python or efficient aggregations in SQL. Be prepared to solve coding problems involving data partitioning, aggregation, and custom logic without relying on high-level libraries.

4.2.6 Illustrate your communication skills and ability to collaborate with cross-functional teams.
Reflect on experiences where you translated complex technical concepts for non-technical stakeholders, resolved misaligned expectations, or facilitated consensus on KPI definitions. Highlight your approach to documenting data processes and ensuring alignment across teams.

4.2.7 Prepare examples of troubleshooting and resolving failures in production data pipelines.
Describe your process for diagnosing recurring ETL failures, implementing logging and alerting, and performing root cause analysis. Discuss how you automated recovery steps and developed long-term solutions to enhance pipeline reliability.

4.2.8 Be ready to discuss how you balance speed and accuracy when delivering time-sensitive data products.
Explain your approach to triaging urgent reporting requests, implementing quality controls, and communicating uncertainty to stakeholders. Share examples of how you maintained trust and transparency under tight deadlines.

4.2.9 Show your ability to influence and drive adoption of data-driven solutions without formal authority.
Share stories where you used evidence and persuasive communication to encourage stakeholders to embrace new processes or recommendations. Focus on your ability to build trust and foster collaboration in cross-functional environments.

5.1 How hard is the Move Data Engineer interview?
The Move Data Engineer interview is challenging and comprehensive, designed to assess both technical depth and problem-solving ability. You’ll be tested on data pipeline architecture, ETL development, large-scale data processing, and real-world troubleshooting. Candidates are also evaluated on their ability to communicate complex concepts and collaborate cross-functionally. The difficulty level is high, especially for those without hands-on experience in building robust data solutions for dynamic environments like real estate technology.

5.2 How many interview rounds does Move have for Data Engineer?
Move typically conducts 5-6 interview rounds for Data Engineer positions. The process includes an initial recruiter screen, one or more technical/case interviews, a behavioral round, and a final onsite (or virtual onsite) series with team members. Each stage is designed to evaluate different facets of your expertise, from technical skills to communication and stakeholder management.

5.3 Does Move ask for take-home assignments for Data Engineer?
Yes, Move often includes a take-home technical assignment as part of the Data Engineer interview process. These assignments focus on designing or troubleshooting data pipelines, ETL workflows, or handling real-world data quality issues. You’ll be given a few days to complete the task, allowing you to showcase your practical skills and approach to solving data engineering challenges.

5.4 What skills are required for the Move Data Engineer?
Key skills for the Move Data Engineer include expertise in Python and SQL, designing scalable ETL pipelines, data modeling and warehouse architecture, data quality assurance, and large-scale data processing. Familiarity with cloud data platforms, automation of data quality checks, and the ability to communicate technical concepts to non-technical stakeholders are highly valued. Experience with real estate data or integrating heterogeneous data sources is a strong plus.

5.5 How long does the Move Data Engineer hiring process take?
The typical Move Data Engineer interview process takes 3-5 weeks from application to offer. Each stage generally lasts about a week, with some flexibility for scheduling and completion of take-home assignments. Fast-track candidates or those with internal referrals may move through the process more quickly, while standard timelines allow for in-depth evaluation and coordination across teams.

5.6 What types of questions are asked in the Move Data Engineer interview?
Expect a mix of technical, case-based, and behavioral questions. Technical questions cover data pipeline design, ETL troubleshooting, data modeling, warehouse architecture, and programming in Python and SQL. Case studies may require you to architect solutions for real-world scenarios or diagnose pipeline failures. Behavioral questions focus on collaboration, stakeholder management, handling ambiguity, and communicating data caveats under pressure.

5.7 Does Move give feedback after the Data Engineer interview?
Move typically provides feedback through recruiters after each interview stage. While the feedback may be high-level, it often includes insights on your technical performance and cultural fit. Detailed technical feedback may be limited, but you can expect to receive updates on your progress and any next steps.

5.8 What is the acceptance rate for Move Data Engineer applicants?
While Move does not publish specific acceptance rates, the Data Engineer role is competitive. Based on industry benchmarks and candidate experience data, the estimated acceptance rate is around 3-6% for qualified applicants who meet the technical and collaborative requirements.

5.9 Does Move hire remote Data Engineer positions?
Yes, Move offers remote Data Engineer positions, with many teams supporting flexible work arrangements. Some roles may require occasional office visits for team collaboration or project kickoffs, but remote opportunities are available, allowing you to contribute from anywhere while supporting Move’s mission to deliver innovative real estate solutions.

• Related guides:
  • Move Data Engineer interview questions
  • How to Prepare for a Data Engineer Interview (Updated in 2025)
  • Top Data Engineer interview tips

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