Attom Data Solutions Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Attom Data Solutions? The Attom Data Solutions Business Intelligence interview process typically spans a diverse set of question topics and evaluates skills in areas like data modeling, ETL pipeline design, dashboard development, and communicating analytical insights to stakeholders. Interview preparation is especially important for this role, as Attom Data Solutions is a leading provider of property data and analytics, requiring candidates to demonstrate both technical proficiency and the ability to translate complex datasets into actionable business recommendations that drive value for clients and internal teams.

In preparing for the interview, you should:

• Understand the core skills necessary for Business Intelligence positions at Attom Data Solutions.
• Gain insights into Attom Data Solutions’ Business Intelligence interview structure and process.
• Practice real Attom Data Solutions 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 Attom Data Solutions Business Intelligence 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 analytics for the real estate, mortgage, insurance, and government sectors. The company aggregates and curates data on over 155 million U.S. properties, offering insights into property values, ownership, neighborhood trends, and market dynamics. Attom’s mission is to empower businesses with actionable intelligence to drive informed decision-making and innovation. As a Business Intelligence professional, you will play a critical role in transforming vast datasets into strategic insights that support Attom’s clients and advance its commitment to data-driven solutions.

As a Business Intelligence professional at Attom Data Solutions, you are responsible for transforming complex property and real estate data into actionable insights that support strategic decision-making across the organization. You will develop and maintain data models, dashboards, and reports, enabling internal teams and clients to analyze market trends and identify business opportunities. Collaborating with product, sales, and data engineering teams, you ensure the accuracy and accessibility of data analytics tools. This role plays a key part in helping Attom deliver valuable data-driven solutions to its clients in the real estate and property sectors.

2.1 Stage 1: Application & Resume Review

The interview journey for a Business Intelligence role at Attom Data Solutions begins with a detailed review of your application and resume. At this stage, the hiring team evaluates your experience with data modeling, ETL processes, data visualization tools, and your ability to work with large, complex datasets. They look for evidence of business acumen, technical proficiency in SQL and BI platforms, and a track record of delivering actionable insights. To prepare, ensure your resume clearly highlights relevant projects, technical skills, and quantifiable achievements in business intelligence and analytics.

2.2 Stage 2: Recruiter Screen

Next, you’ll typically have a phone or video call with a recruiter. This conversation is designed to assess your overall fit for the company and the role, touching on your motivation, communication skills, and basic understanding of business intelligence concepts. The recruiter may also clarify your experience with data warehousing, dashboard creation, and cross-functional collaboration. Preparation should focus on articulating your career story, why you’re interested in Attom Data Solutions, and how your background aligns with the company’s data-driven mission.

2.3 Stage 3: Technical/Case/Skills Round

This stage involves an in-depth evaluation of your technical capabilities and problem-solving approach. You may be asked to walk through case studies or tackle practical exercises such as designing data warehouses for e-commerce or retail, outlining ETL pipelines, or addressing data quality issues. Expect questions that test your ability to analyze and synthesize data from multiple sources, design scalable analytics solutions, and present findings in a way that supports business objectives. Interviewers may include BI team leads, data engineers, or analytics managers. To prepare, review your experience with data pipeline design, data cleaning, dashboard development, and translating business requirements into technical solutions.

2.4 Stage 4: Behavioral Interview

In this round, the focus shifts to your soft skills and cultural fit. You’ll be asked to describe situations where you’ve overcome challenges in data projects, navigated stakeholder communication, or made complex insights accessible to non-technical audiences. The interviewers are interested in your adaptability, teamwork, and ability to drive results in ambiguous or rapidly changing environments. Prepare by reflecting on your past experiences, especially those that demonstrate effective collaboration, conflict resolution, and proactive problem-solving in business intelligence contexts.

2.5 Stage 5: Final/Onsite Round

The final stage typically consists of multiple interviews with various team members, including BI leadership, cross-functional partners, and sometimes executives. You may be asked to present a data project, solve a business case live, or discuss how you would approach real-world business challenges at Attom Data Solutions. This round assesses both your technical depth and your ability to communicate complex solutions to a diverse audience. Preparation should include ready-to-share examples of impactful BI work, as well as strategies for engaging stakeholders and driving data adoption across the organization.

2.6 Stage 6: Offer & Negotiation

If you are successful through the previous rounds, you’ll receive an offer from the recruiter. This stage involves discussing compensation, benefits, start dates, and any final questions about the role or company. Be ready to negotiate based on your research and to clearly articulate your value to the business intelligence function at Attom Data Solutions.

2.7 Average Timeline

The typical interview process for a Business Intelligence role at Attom Data Solutions spans 3-5 weeks from initial application to final offer. Fast-track candidates with highly relevant experience or internal referrals may progress in as little as 2-3 weeks, while the standard process generally allows for a week between each round, with occasional delays for scheduling or rescheduling. Onsite or final rounds may require additional coordination, especially if presentations or case studies are involved.

Next, let’s dive into the types of interview questions you can expect throughout the Attom Data Solutions Business Intelligence interview process.

3.1. Data Modeling & Warehousing

For Business Intelligence roles at Attom Data Solutions, expect questions that assess your ability to design scalable data models and warehouses that serve both current and future analytic needs. You'll need to demonstrate an understanding of schema design, normalization, and how to support diverse reporting requirements.

3.1.1 Design a data warehouse for a new online retailer
Focus on outlining the entities, relationships, and fact/dimension tables that would support core retail analytics. Mention considerations for scalability, historical data tracking, and integration with upstream systems.

3.1.2 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Highlight how you'd handle localization, multi-currency, and regional compliance requirements. Discuss partitioning strategies and how to accommodate new markets or product lines.

3.1.3 Design a database for a ride-sharing app.
Describe the schema you’d implement for users, drivers, rides, and payments. Emphasize normalization, indexing, and supporting real-time analytics.

3.1.4 Migrating a social network's data from a document database to a relational database for better data metrics
Discuss the migration strategy, potential data modeling challenges, and how you'd ensure data consistency and minimal downtime.

3.2. Data Engineering & ETL

Business Intelligence professionals at Attom Data Solutions are often tasked with building reliable ETL pipelines and ensuring data quality at scale. You'll be expected to demonstrate experience designing, optimizing, and troubleshooting complex data ingestion and transformation workflows.

3.2.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Outline your approach to handling schema differences, data validation, and monitoring for failures. Mention technologies you might use and how you'd ensure scalability.

3.2.2 Aggregating and collecting unstructured data.
Explain how you’d structure the pipeline to handle diverse file formats and sources. Discuss data normalization, metadata extraction, and downstream usability.

3.2.3 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Describe the ingestion, validation, error handling, and reporting stages. Highlight how you’d ensure data integrity and timely availability.

3.2.4 Design a data pipeline for hourly user analytics.
Focus on the architecture, scheduling, and aggregation logic. Discuss how you’d optimize for both speed and reliability.

3.3. Data Quality & Cleaning

Ensuring high data quality is central to effective business intelligence. Attom Data Solutions values candidates who can diagnose, remediate, and prevent data quality issues at scale, as well as communicate the impact of data imperfections.

3.3.1 How would you approach improving the quality of airline data?
Describe your framework for profiling, identifying root causes, and collaborating with stakeholders to implement fixes. Mention automation and ongoing monitoring.

3.3.2 Ensuring data quality within a complex ETL setup
Explain strategies for automated validation, reconciliation, and alerting. Discuss how to balance thoroughness with pipeline performance.

3.3.3 Describing a real-world data cleaning and organization project
Walk through your approach from initial profiling to documentation and stakeholder communication. Highlight any tools or scripts you developed.

3.3.4 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?
Lay out your process for data mapping, joining, and resolving discrepancies. Emphasize the importance of source-of-truth selection and maintaining auditability.

3.4. Analytics & Experimentation

Attom Data Solutions looks for BI professionals who can design and interpret analytics experiments, build dashboards, and translate business questions into actionable metrics. Expect to be tested on your ability to measure impact and communicate findings.

3.4.1 The role of A/B testing in measuring the success rate of an analytics experiment
Describe how you’d design the experiment, define success metrics, and interpret results. Discuss statistical significance and business impact.

3.4.2 Which metrics and visualizations would you prioritize for a CEO-facing dashboard during a major rider acquisition campaign?
Explain your approach to metric selection, visual design, and tailoring insights for executive audiences.

3.4.3 Designing a dynamic sales dashboard to track McDonald's branch performance in real-time
Highlight how you’d structure the underlying data, ensure real-time updates, and make the dashboard actionable.

3.4.4 What kind of analysis would you conduct to recommend changes to the UI?
Discuss user journey mapping, funnel analysis, and A/B testing. Explain how you’d prioritize recommendations based on impact and feasibility.

3.5. Communication & Stakeholder Management

In Business Intelligence at Attom Data Solutions, the ability to communicate insights and collaborate across business and technical teams is crucial. You’ll be assessed on how you tailor your message, manage expectations, and bridge technical gaps.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe your approach to storytelling with data, using visuals and analogies to match the audience’s expertise.

3.5.2 Making data-driven insights actionable for those without technical expertise
Explain how you distill findings to their business relevance, using plain language and clear next steps.

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Discuss techniques for building intuitive dashboards and training stakeholders to self-serve analytics.

3.5.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Share your framework for surfacing assumptions, aligning goals, and keeping communication transparent throughout the project lifecycle.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision. What was the outcome and how did you ensure your recommendation was implemented?
3.6.2 Describe a challenging data project and how you handled it from start to finish.
3.6.3 How do you handle unclear requirements or ambiguity when scoping a new analytics project?
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?
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.
3.6.6 Describe a time you had to negotiate scope creep when multiple stakeholders kept adding requests. How did you keep the project on track?
3.6.7 Give an example of how you balanced short-term wins with long-term data integrity when pressured to deliver quickly.
3.6.8 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
3.6.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
3.6.10 Describe a time you delivered critical insights even though a significant portion of the dataset had missing or unreliable values. What analytical trade-offs did you make?

4.1 Company-specific tips:

• Immerse yourself in Attom Data Solutions’ core business: property data and analytics. Familiarize yourself with their data offerings, including property values, ownership records, neighborhood trends, and market dynamics. Understand how these data assets empower clients in real estate, mortgage, insurance, and government sectors to make informed decisions.

• Research recent product launches, partnerships, and industry trends relevant to Attom. Be prepared to discuss how property data is transforming real estate and related industries, and show curiosity about innovative applications like predictive analytics, risk scoring, and market forecasting.

• Learn about Attom’s commitment to data accuracy, coverage, and timeliness. Consider how you would contribute to maintaining high standards in data quality and reliability, and be ready to discuss your approach to scaling analytics solutions for large, diverse datasets.

4.2 Role-specific tips:

4.2.1 Demonstrate expertise in designing scalable data models and warehouses for property analytics.
Prepare to discuss your experience structuring data warehouses that support complex reporting and analytics needs. Focus on how you would model entities such as properties, transactions, owners, and neighborhoods, ensuring normalization and efficient querying. Be ready to address challenges like historical data tracking and supporting evolving business requirements.

4.2.2 Articulate your approach to building robust ETL pipelines for heterogeneous property data sources.
Showcase your ability to design ETL workflows that ingest, clean, and transform data from multiple providers, formats, and systems. Describe strategies for handling schema differences, validating incoming data, and monitoring pipeline health. Emphasize your experience with automation and scaling pipelines to accommodate growing data volumes.

4.2.3 Highlight your skills in data cleaning, profiling, and quality assurance.
Attom expects BI professionals to proactively identify and resolve data quality issues. Share examples of how you’ve diagnosed root causes of data inaccuracies, implemented automated validation checks, and collaborated with stakeholders to improve data reliability. Discuss your process for ongoing monitoring and documentation.

4.2.4 Show your ability to synthesize insights from diverse, complex datasets.
Prepare to walk through real-world scenarios where you combined multiple data sources—such as transactions, user behavior, and third-party feeds—to extract actionable insights. Explain your process for mapping and joining disparate datasets, resolving inconsistencies, and ensuring auditability throughout your analysis.

4.2.5 Demonstrate proficiency in dashboard and report development tailored to executive and client audiences.
Attom values BI professionals who can translate business questions into clear, impactful dashboards. Discuss your approach to metric selection, visual design, and storytelling with data. Be ready to describe how you prioritize information for different stakeholders, ensuring that insights are both accessible and actionable.

4.2.6 Display your understanding of analytics experimentation and impact measurement.
Be prepared to talk through the design and interpretation of analytics experiments, such as A/B tests or pilot projects. Explain how you define success metrics, ensure statistical rigor, and communicate the results in a way that drives business decisions.

4.2.7 Emphasize your communication skills and stakeholder management strategies.
Attom’s BI roles require effective collaboration across technical and business teams. Share examples of how you’ve tailored complex insights for non-technical audiences, built intuitive dashboards, and aligned stakeholders with differing priorities. Discuss your approach to managing expectations, resolving conflicts, and keeping communication transparent throughout project lifecycles.

4.2.8 Prepare stories that showcase your adaptability, problem-solving, and leadership in ambiguous situations.
Reflect on times when you navigated unclear requirements, negotiated scope creep, or delivered results despite incomplete data. Be ready to discuss how you balanced short-term wins with long-term data integrity, influenced stakeholders without formal authority, and used prototypes to align diverse visions.

4.2.9 Practice structured responses to behavioral questions using the STAR method.
For each behavioral question, clearly outline the Situation, Task, Action, and Result. Focus on examples that highlight your impact, initiative, and ability to drive data-driven outcomes in business intelligence projects at scale.

4.2.10 Prepare to present a data project or case study relevant to Attom’s business.
Have a ready-to-share example of a BI project where you tackled complex data challenges, delivered strategic insights, and engaged stakeholders. Tailor your presentation to showcase technical depth, business acumen, and your ability to make property data actionable for clients and internal teams.

5.1 “How hard is the Attom Data Solutions Business Intelligence interview?”
The Attom Data Solutions Business Intelligence interview is considered moderately challenging, especially for candidates new to property data or large-scale analytics environments. The process rigorously assesses both technical depth and business acumen. You’ll face questions on data modeling, ETL pipeline design, dashboard development, data quality, and stakeholder communication, all tailored to Attom’s focus on real estate and property analytics. Candidates who can demonstrate experience translating complex datasets into actionable business recommendations tend to excel.

5.2 “How many interview rounds does Attom Data Solutions have for Business Intelligence?”
Typically, there are five to six rounds in the Attom Data Solutions Business Intelligence interview process. These include an initial application and resume review, a recruiter screen, a technical/case round, a behavioral interview, and a final onsite or virtual round with multiple team members. Each stage is designed to evaluate a different aspect of your technical and interpersonal skill set.

5.3 “Does Attom Data Solutions ask for take-home assignments for Business Intelligence?”
Yes, candidates may be given a take-home assignment or case study, especially in the technical/case round. These assignments often involve designing a scalable data model, building a dashboard, or outlining an ETL pipeline using property data. The goal is to assess your practical problem-solving skills and your ability to deliver clear, actionable insights.

5.4 “What skills are required for the Attom Data Solutions Business Intelligence?”
Key skills for this role include expertise in data modeling, ETL pipeline development, SQL, and BI tools such as Tableau or Power BI. You should also be proficient in data cleaning, quality assurance, and synthesizing insights from complex property datasets. Strong communication and stakeholder management abilities are essential, as is the capacity to translate technical findings into business value for real estate, mortgage, and insurance clients.

5.5 “How long does the Attom Data Solutions Business Intelligence hiring process take?”
The typical timeline is three to five weeks from initial application to final offer. Each stage generally takes about a week, though scheduling or presentation requirements may extend the process slightly. Fast-track candidates or those with internal referrals may move more quickly.

5.6 “What types of questions are asked in the Attom Data Solutions Business Intelligence interview?”
Expect a mix of technical and behavioral questions. Technical questions cover data modeling, ETL design, data quality, dashboard building, and analytics experimentation—often with a property data focus. Behavioral questions assess your problem-solving, adaptability, stakeholder communication, and ability to drive results in ambiguous or fast-changing environments. You may also be asked to present a past project or walk through a live case study relevant to Attom’s business.

5.7 “Does Attom Data Solutions give feedback after the Business Intelligence interview?”
Attom Data Solutions typically provides feedback through the recruiter, especially if you reach the later stages of the process. While detailed technical feedback may be limited, you can expect to receive high-level insights on your performance and fit for the role.

5.8 “What is the acceptance rate for Attom Data Solutions Business Intelligence applicants?”
While exact numbers are not public, the acceptance rate for Business Intelligence roles at Attom Data Solutions is competitive, estimated around 3-5% for qualified applicants. The company seeks candidates with a strong blend of technical expertise and business insight, particularly those with experience in property or real estate analytics.

5.9 “Does Attom Data Solutions hire remote Business Intelligence positions?”
Yes, Attom Data Solutions does offer remote opportunities for Business Intelligence professionals. Some roles may be fully remote, while others could require occasional visits to the office for team collaboration or project kickoffs. Be sure to clarify remote work expectations with your recruiter during the process.

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

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