Attom Data Solutions Data Analyst Interview Guide

Getting ready for a Data Analyst interview at Attom Data Solutions? The Attom Data Solutions Data Analyst interview process typically spans multiple question topics and evaluates skills in areas like data cleaning and organization, data pipeline design, stakeholder communication, and presenting actionable insights through visualization. Interview preparation is especially important for this role, as candidates are expected to demonstrate expertise in transforming complex data sets into clear, business-driven recommendations and to communicate findings effectively with both technical and non-technical audiences in a data-focused environment.

In preparing for the interview, you should:

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

Attom Data Solutions is a leading provider of comprehensive property data and real estate analytics for businesses and professionals across the housing industry. The company aggregates and curates public property records, tax data, foreclosure information, and neighborhood insights to support decision-making in real estate, mortgage, insurance, and related sectors. Attom’s mission is to power innovation with reliable data, enabling clients to identify trends, assess risk, and uncover market opportunities. As a Data Analyst, you will contribute to transforming raw data into actionable insights that drive value for Attom’s diverse client base.

As a Data Analyst at Attom Data Solutions, you will be responsible for collecting, cleaning, and analyzing real estate and property data to deliver actionable insights for internal teams and clients. You will work closely with product, engineering, and business development groups to support data-driven decision-making and enhance Attom’s data products. Typical tasks include building reports, visualizations, and dashboards, as well as identifying trends and anomalies in large datasets. This role is essential for ensuring data accuracy and uncovering insights that help Attom provide valuable property intelligence to the real estate, mortgage, and insurance industries.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application and resume, where the focus is on your technical proficiency in data analysis, experience with large-scale data pipelines, and your ability to deliver actionable business insights from complex datasets. Emphasis is placed on demonstrated expertise with SQL, Python, data visualization, and prior work with data warehousing or ETL processes. To prepare, ensure your resume highlights measurable impacts, technical skills, and any experience communicating data-driven recommendations to business stakeholders.

2.2 Stage 2: Recruiter Screen

The recruiter screen is typically a 30-minute phone call designed to assess your overall fit for the Data Analyst role at Attom Data Solutions. The recruiter will discuss your background, motivation for applying, and confirm your understanding of the company’s data-driven culture. Expect questions about your experience working cross-functionally and your ability to translate technical findings into business value. Prepare by clearly articulating your career journey and how your skills align with the company’s mission and analytics needs.

2.3 Stage 3: Technical/Case/Skills Round

This stage involves one or more technical interviews, which may be conducted virtually or in-person by a senior data analyst, analytics manager, or a member of the data engineering team. You’ll be evaluated on your ability to solve real-world data problems, such as designing scalable ETL pipelines, cleaning and organizing large datasets, and building data models for business scenarios (e.g., ride-sharing, retail, or financial services). You may also be asked to interpret A/B test results, design data schemas, and demonstrate proficiency in SQL and Python through live or take-home exercises. To excel, practice structuring your approach to ambiguous data challenges and explaining your reasoning clearly.

2.4 Stage 4: Behavioral Interview

The behavioral interview focuses on your communication, stakeholder management, and problem-solving skills. Interviewers—often including data team leads or product managers—will probe into how you’ve handled challenges in past data projects, navigated misaligned expectations, and presented complex insights to non-technical audiences. You should be prepared to discuss specific examples of cross-functional collaboration, managing project hurdles, and making data accessible through visualization and storytelling. Reflect on your experience making data-driven recommendations and adapting your communication style for different audiences.

2.5 Stage 5: Final/Onsite Round

The final round generally consists of multiple back-to-back interviews with various team members, including senior leadership, data engineers, and business stakeholders. This stage may include a mix of technical deep-dives, case studies, and behavioral assessments, as well as a presentation component where you’ll be asked to convey insights from a data project or respond to a business scenario. You’ll be evaluated not only on your technical acumen but also on your ability to influence decision-making and drive business outcomes with data. To prepare, review your most impactful projects and be ready to discuss your end-to-end analytical process, from data ingestion to insight delivery.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll enter the offer and negotiation phase, typically facilitated by the recruiter or HR representative. This stage covers compensation, benefits, role expectations, and start date. It’s also an opportunity to clarify any remaining questions about team structure and growth opportunities. Preparation involves researching industry benchmarks and reflecting on your priorities to ensure a mutually beneficial agreement.

2.7 Average Timeline

The typical Attom Data Solutions Data Analyst interview process spans 3-5 weeks from initial application to offer. Candidates with highly relevant experience or referrals may move through the process more quickly, sometimes in as little as 2-3 weeks. The standard pace allows approximately one week between each stage, with technical and onsite rounds scheduled based on team availability. Take-home assignments or presentation components may extend the timeline slightly, depending on the complexity and candidate schedule.

Next, let’s dive into the types of interview questions you can expect throughout these stages.

3.1 Data Analysis & Experimentation

Data Analysts at Attom Data Solutions are expected to design experiments, analyze outcomes, and translate findings into actionable recommendations. Interviewers will probe your understanding of experimental design, A/B testing, and your ability to interpret real-world data with business impact in mind.

3.1.1 The role of A/B testing in measuring the success rate of an analytics experiment
Explain how you would set up an A/B test, select appropriate metrics, and determine statistical significance. Discuss how you’d interpret results and communicate them to stakeholders.

3.1.2 How would you evaluate whether a 50% rider discount promotion is a good or bad idea? What metrics would you track?
Describe your approach to measuring the impact of a promotion, including experimental design, key metrics (e.g., conversion, retention), and how you'd analyze the results for business value.

3.1.3 What kind of analysis would you conduct to recommend changes to the UI?
Outline how you’d track user journeys, identify drop-off points, and use data to support recommendations for UI improvements.

3.1.4 How would you analyze how the feature is performing?
Discuss the metrics you’d monitor, how you’d segment users, and which statistical techniques you’d use to assess feature adoption and effectiveness.

3.1.5 How would you approach improving the quality of airline data?
Describe your process for identifying data quality issues, prioritizing fixes, and measuring the impact of improvements on downstream analytics.

3.2 Data Engineering & Pipeline Design

You’ll be expected to understand the architecture and design of robust data systems. Questions will focus on your ability to build scalable pipelines, manage data quality, and structure data warehouses for analytics.

3.2.1 Design a data warehouse for a new online retailer
Explain your approach to schema design, data integration, and supporting analytics requirements for a new business.

3.2.2 Design a data pipeline for hourly user analytics
Describe the components of a reliable pipeline, how you’d handle data latency, and ensure accuracy in near-real-time reporting.

3.2.3 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Discuss ingestion, error handling, data validation, and reporting strategies for handling large volumes of customer data.

3.2.4 Ensuring data quality within a complex ETL setup
Share your approach to monitoring and maintaining data quality across multiple data sources and transformations.

3.2.5 Modifying a billion rows
Explain your strategy for efficiently updating massive datasets, including considerations for performance and data integrity.

3.3 Data Communication & Visualization

Clear communication of complex insights is essential. These questions assess your ability to present findings, tailor messages to diverse audiences, and create accessible visualizations.

3.3.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe your process for distilling technical results and adapting your presentation style based on your audience’s background.

3.3.2 Making data-driven insights actionable for those without technical expertise
Explain how you bridge the gap between data and decision-makers, using analogies, visuals, or simplified metrics.

3.3.3 Demystifying data for non-technical users through visualization and clear communication
Discuss your approach to designing dashboards, reports, or visualizations that empower non-technical users to make informed decisions.

3.3.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Share your preferred tools and techniques for summarizing, grouping, and presenting long-tail data in a business context.

3.3.5 Which metrics and visualizations would you prioritize for a CEO-facing dashboard during a major rider acquisition campaign?
Describe your criteria for selecting high-level metrics and visual elements that drive executive action.

3.4 Data Cleaning & Real-World Data Challenges

Attom Data Solutions handles large and often messy datasets. You’ll be tested on your ability to clean, organize, and ensure the reliability of real-world data.

3.4.1 Describing a real-world data cleaning and organization project
Outline the steps you took to clean, validate, and document a complex dataset, emphasizing reproducibility and impact.

3.4.2 Describing a data project and its challenges
Share a story about a challenging data project, the hurdles you faced, and how you overcame them to deliver results.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision. What was the impact on the business or project?

3.5.2 Describe a challenging data project and how you handled it, especially when you encountered unexpected obstacles.

3.5.3 How do you handle unclear requirements or ambiguity in analytics requests?

3.5.4 Tell me about a time when your colleagues didn’t agree with your approach. How did you bring them into the conversation and address their concerns?

3.5.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

3.5.6 Describe a time you had to negotiate scope creep when multiple teams kept adding requests to your analytics project. How did you keep the project on track?

3.5.7 Give an example of how you balanced short-term wins with long-term data integrity when pressured to deliver quickly.

3.5.8 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.

3.5.9 Walk us through how you handled conflicting KPI definitions between two teams and arrived at a single source of truth.

3.5.10 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.

4.1 Company-specific tips:

Get to know Attom Data Solutions’ core business—aggregating and curating property, tax, foreclosure, and neighborhood data for real estate, mortgage, and insurance clients. Review how Attom’s data products help organizations identify market trends, assess risk, and uncover opportunities. Study recent industry news, product launches, and Attom’s approach to powering innovation with reliable property intelligence. Be prepared to discuss how your analytical skills can add value to Attom’s mission and diverse client base.

Understand the types of data Attom works with, such as public records, transactional data, and geographic information. Familiarize yourself with the challenges of working with real estate datasets, including data quality, integration, and privacy concerns. Consider how Attom’s data enables business decisions for clients and think about how you could contribute to improving data accuracy and actionable insights.

Review Attom’s client-facing solutions and data platforms, including APIs and dashboards. Demonstrate your awareness of how data analysts support both internal product teams and external customers by transforming complex data into accessible, business-driven recommendations. Be ready to show your understanding of the impact your work can have on Attom’s products and client outcomes.

4.2 Role-specific tips:

4.2.1 Practice designing and explaining scalable ETL pipelines for real estate data. Prepare to discuss how you would ingest, clean, and transform large volumes of property records and transactional data. Focus on outlining the steps you’d take to ensure data quality, handle missing or inconsistent values, and document transformations. Be ready to talk through the architecture of a robust pipeline, including error handling, validation, and performance optimization.

4.2.2 Demonstrate your ability to analyze and visualize business-critical metrics. Showcase your experience building dashboards and reports that track key metrics like property value trends, foreclosure rates, and market segmentation. Practice tailoring your visualizations for different audiences—executives, product managers, and clients—highlighting the insights most relevant to their decision-making needs. Emphasize clarity, storytelling, and actionable recommendations in your presentations.

4.2.3 Prepare examples of real-world data cleaning and organization projects. Reflect on past experiences where you transformed messy, incomplete, or inconsistent datasets into reliable sources of truth. Be ready to describe your process for identifying data quality issues, documenting cleaning steps, and ensuring reproducibility. Discuss the business impact of your work—how improved data quality led to better insights and decisions.

4.2.4 Brush up on experimental design, A/B testing, and interpreting results for business value. Review how to set up controlled experiments, select appropriate metrics, and determine statistical significance. Practice explaining your approach to measuring the impact of promotions, product changes, or feature launches. Focus on your ability to translate experimental outcomes into recommendations that drive business outcomes.

4.2.5 Highlight your experience communicating complex insights to non-technical audiences. Prepare stories about times you distilled technical findings into clear, actionable messages for stakeholders with varying levels of data literacy. Emphasize your adaptability—using analogies, simplified metrics, or visualizations—to make data accessible and drive informed decisions.

4.2.6 Be ready to discuss challenging data projects and how you navigated obstacles. Think about situations where you faced unclear requirements, misaligned expectations, or conflicting KPI definitions. Prepare to share how you collaborated across teams, negotiated scope, and arrived at consensus. Show your resilience and problem-solving skills in the face of ambiguity.

4.2.7 Practice building and presenting executive-level dashboards. Consider which metrics and visualizations would be most valuable for senior leadership during key business initiatives, such as market expansion or acquisition campaigns. Focus on high-level, actionable insights that support strategic decision-making, and be prepared to justify your choices.

4.2.8 Demonstrate your ability to influence stakeholders and drive adoption of data-driven recommendations. Prepare examples where you used prototypes, wireframes, or iterative analysis to align diverse teams around a shared vision. Emphasize your communication skills and your ability to build consensus without formal authority.

4.2.9 Review your approach to documenting and maintaining data integrity under pressure. Be ready to discuss how you balance quick wins with long-term reliability, especially when facing tight deadlines or scope creep. Show your commitment to building sustainable, trustworthy analytics solutions that support Attom’s reputation for data quality.

4.2.10 Reflect on your experience with large-scale data modifications and performance optimization. Prepare to explain your strategy for efficiently updating massive datasets, such as billions of property records, while ensuring data integrity and minimizing disruptions to downstream analytics. Highlight your technical acumen and attention to detail.

5.1 How hard is the Attom Data Solutions Data Analyst interview?
The Attom Data Solutions Data Analyst interview is moderately challenging, especially for candidates who are new to real estate data or large-scale analytics environments. The interview rigorously tests your proficiency in data cleaning, pipeline design, SQL, Python, and your ability to translate complex data into actionable business insights. Candidates with experience in property data, ETL processes, and stakeholder communication will find themselves well-prepared for the technical and business-focused questions.

5.2 How many interview rounds does Attom Data Solutions have for Data Analyst?
Typically, there are 5-6 interview rounds for the Data Analyst position at Attom Data Solutions. These include an initial recruiter screen, one or more technical/case interviews, a behavioral interview, and a final onsite or virtual interview with multiple team members. Some candidates may also be asked to complete a take-home assignment or present a data project during the final round.

5.3 Does Attom Data Solutions ask for take-home assignments for Data Analyst?
Yes, Attom Data Solutions may request a take-home assignment, especially for candidates advancing to later stages. These assignments often involve cleaning and analyzing a real-world dataset, designing a scalable data pipeline, or building a dashboard to present actionable insights. The goal is to assess your technical skills and your ability to communicate findings effectively.

5.4 What skills are required for the Attom Data Solutions Data Analyst?
Key skills for the Data Analyst role at Attom Data Solutions include strong proficiency in SQL and Python, experience with data visualization tools, expertise in data cleaning and organization, and the ability to design scalable ETL pipelines. Familiarity with real estate data, stakeholder communication, and presenting insights to both technical and non-technical audiences is highly valued.

5.5 How long does the Attom Data Solutions Data Analyst hiring process take?
The typical hiring process for a Data Analyst at Attom Data Solutions spans 3-5 weeks, depending on candidate availability and team schedules. Candidates with highly relevant experience or referrals may progress faster, sometimes in as little as 2-3 weeks. Take-home assignments and presentation components can extend the timeline slightly.

5.6 What types of questions are asked in the Attom Data Solutions Data Analyst interview?
Expect a mix of technical questions on data cleaning, pipeline design, SQL, and Python, as well as case studies related to property analytics and business scenarios. You’ll also encounter behavioral questions focused on stakeholder management, communication, and problem-solving. Presentation tasks and questions about designing executive dashboards are common in later rounds.

5.7 Does Attom Data Solutions give feedback after the Data Analyst interview?
Attom Data Solutions typically provides feedback through recruiters, especially for candidates who reach the onsite or final interview stages. While detailed technical feedback may be limited, you can expect high-level insights on your strengths and areas for improvement.

5.8 What is the acceptance rate for Attom Data Solutions Data Analyst applicants?
The Data Analyst role at Attom Data Solutions is competitive, with an estimated acceptance rate of 3-5% for qualified applicants. Candidates with strong technical backgrounds and experience in real estate data analytics stand out in the selection process.

5.9 Does Attom Data Solutions hire remote Data Analyst positions?
Yes, Attom Data Solutions offers remote Data Analyst positions, with some roles requiring occasional office visits or collaboration with onsite teams. Flexibility is provided depending on the team’s needs and the specific responsibilities of the role.

• Related guides:
  • Attom Data Solutions interview questions
  • Data Analyst interview guide
  • Top Data Analyst interview tips

Ready to ace your Attom Data Solutions Data Analyst interview? It’s not just about knowing the technical skills—you need to think like an Attom Data Solutions 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 Attom Data Solutions and similar companies.

With resources like the Attom Data Solutions 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. You’ll practice tackling questions on data cleaning, scalable pipeline design, stakeholder communication, and presenting actionable insights—just like you’ll face in the real interview.

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