Attom Data Solutions Software Engineer Interview Guide

Getting ready for a Software Engineer interview at Attom Data Solutions? The Attom Data Solutions Software Engineer interview process typically spans several technical and system design question topics and evaluates skills in areas like data engineering, database schema design, system architecture, and stakeholder communication. Interview preparation is especially important for this role at Attom Data Solutions, as candidates are expected to demonstrate their ability to build scalable data pipelines, design robust data storage solutions, and communicate technical concepts clearly to both technical and non-technical audiences. Given Attom’s focus on real estate data and analytics, software engineers here often work on projects involving large-scale data ingestion, quality assurance, and the translation of complex data insights into actionable solutions for diverse clients.

In preparing for the interview, you should:

• Understand the core skills necessary for Software Engineer positions at Attom Data Solutions.
• Gain insights into Attom Data Solutions’ Software Engineer interview structure and process.
• Practice real Attom Data Solutions Software 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 Attom Data Solutions Software Engineer 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 consumers across the United States. The company aggregates and curates data on property values, deeds, mortgages, foreclosures, and neighborhood trends, powering insights for real estate, mortgage, insurance, and government clients. Attom’s mission is to deliver actionable property data that drives innovation and informed decision-making in the housing market. As a Software Engineer, you will contribute to building and maintaining robust data platforms and applications that support Attom’s data-driven solutions.

As a Software Engineer at Attom Data Solutions, you will design, develop, and maintain software applications that support the company’s robust real estate data platform. Your work will involve collaborating with data analysts, product managers, and other engineers to build scalable systems for data processing, integration, and delivery. You will be responsible for writing clean, efficient code, troubleshooting technical issues, and contributing to the continuous improvement of Attom’s data products and APIs. This role is central to ensuring reliable, high-performance solutions that empower clients to access and analyze real estate data, supporting Attom’s mission to deliver actionable property insights.

2.1 Stage 1: Application & Resume Review

The initial phase involves a thorough screening of your resume and application materials by the technical recruiting team. They assess your experience with software engineering fundamentals, proficiency in designing scalable systems, data pipeline development, and familiarity with cloud and database technologies. Highlight your hands-on experience with system architecture, ETL pipeline design, and data-driven product development to stand out in this step.

2.2 Stage 2: Recruiter Screen

Next, you’ll have a conversation with a recruiter, typically lasting 30–45 minutes. This call focuses on your motivation, interest in Attom Data Solutions, and your alignment with the company’s mission in real estate data innovation. Expect to discuss your background, career trajectory, and communication skills, as well as your ability to collaborate with both technical and non-technical stakeholders. Prepare by reviewing your resume and practicing concise explanations of your key projects.

2.3 Stage 3: Technical/Case/Skills Round

This stage is conducted by software engineering team leads or senior engineers. You’ll be assessed on your coding skills, system design abilities, and problem-solving approach. Expect challenges involving data pipeline architecture, database schema design (e.g., for ride-sharing or retail platforms), scalable ETL solutions, and algorithmic tasks such as shortest path algorithms or data aggregation. Be ready to discuss your experience with data modeling, data cleaning, and building robust, maintainable software systems. Preparation should include revisiting your technical fundamentals and practicing whiteboard/system design scenarios.

2.4 Stage 4: Behavioral Interview

Behavioral interviews are typically conducted by engineering managers or cross-functional partners. Here, you’ll be evaluated on your teamwork, adaptability, stakeholder communication, and ability to present complex data insights clearly. Expect to discuss real-world challenges you’ve faced in data projects, how you resolved misaligned expectations, and how you make technical concepts accessible to non-technical audiences. Prepare by reflecting on your past experiences and formulating clear, structured stories that demonstrate your impact and leadership.

2.5 Stage 5: Final/Onsite Round

The final stage usually consists of multiple back-to-back interviews with team members, engineering leadership, and sometimes product managers. This round may include a mix of technical deep-dives, system design exercises, and behavioral assessments. You’ll be expected to demonstrate your technical expertise, architectural thinking, and ability to collaborate across teams. There may also be a case study or presentation component, where you’ll need to explain your approach to a complex data engineering problem or system design relevant to Attom’s business.

2.6 Stage 6: Offer & Negotiation

Once you’ve successfully completed all interview rounds, the recruiter will reach out with a formal offer. This step includes discussions around compensation, benefits, start date, and any final clarifications. Negotiations are handled by the recruiting team, with input from hiring managers as needed.

2.7 Average Timeline

The typical Attom Data Solutions Software Engineer interview process spans about 3–4 weeks from initial application to offer. Fast-track candidates with highly relevant experience or internal referrals may progress in 2–3 weeks, while the standard pace allows for a week or more between rounds to accommodate scheduling and feedback. Onsite rounds are usually consolidated into a single day, and technical assessments are scheduled based on team availability.

Now, let’s dive into the specific interview questions you may encounter during the process.

3.1. System Design & Architecture

Expect questions that assess your ability to design scalable, robust systems and databases suited to Attom Data Solutions’ data-driven products. Focus on clearly communicating trade-offs, modularity, and how your design supports future growth and maintainability.

3.1.1 System design for a digital classroom service.
Break down requirements into user flows, core entities, and data storage needs. Discuss scalability, real-time data handling, and security considerations. Example: “I’d separate user authentication, classroom management, and content delivery into microservices, using a relational database for transactional data and object storage for media.”

3.1.2 Design a database for a ride-sharing app.
Identify key tables (users, rides, payments, locations), normalization strategies, and indexing for query efficiency. Consider real-time updates and geo-queries. Example: “I’d use a partitioned table for ride events, with spatial indexes for location lookups, and separate transaction logs for payment reconciliation.”

3.1.3 Design a data warehouse for a new online retailer.
Outline fact and dimension tables, ETL strategies, and how to support analytics use cases. Address schema evolution and data governance. Example: “Customer, product, and sales dimensions would feed into a central sales fact table, with daily ETL jobs and data quality checks.”

3.1.4 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Discuss modular ingestion, schema mapping, error handling, and monitoring. Emphasize extensibility for new partners. Example: “I’d use a message queue for ingestion, a schema registry for mapping, and a validation layer to catch anomalies before storage.”

3.1.5 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Describe batch and streaming options, data validation, storage formats, and reporting endpoints. Example: “I’d use a cloud function to parse uploads, validate headers and types, store in columnar format, and trigger reporting jobs via scheduled tasks.”

3.2. Data Modeling & Querying

These questions test your ability to translate business requirements into efficient data models and write queries that support analytics and reporting. Highlight normalization, indexing, and query optimization strategies.

3.2.1 Model a database for an airline company.
Define tables for flights, bookings, passengers, and schedules. Discuss referential integrity and indexing for fast lookups. Example: “I’d separate flight legs into a junction table and use foreign keys to link bookings with passengers and flights.”

3.2.2 Design a system to synchronize two continuously updated, schema-different hotel inventory databases at Agoda.
Explain approaches for schema reconciliation, conflict resolution, and real-time syncing. Example: “I’d use a mapping layer to harmonize schemas and a change-data-capture system for syncing updates.”

3.2.3 How would you determine which database tables an application uses for a specific record without access to its source code?
Describe profiling, query logging, and data lineage tools. Example: “I’d enable query logging, trace record IDs across tables, and use dependency graphs to visualize usage.”

3.2.4 Write a query to get the current salary for each employee after an ETL error.
Show how to use window functions and error correction logic in SQL. Example: “I’d partition by employee ID, order by timestamp, and select the most recent valid record.”

3.2.5 Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.
Explain aggregation, filtering, and ranking in SQL. Example: “Group by department, filter on employee count, calculate percentage, and use ORDER BY with LIMIT.”

3.3. Data Engineering & Scalability

Here, you’ll encounter questions about building, optimizing, and maintaining data pipelines and large-scale processing systems. Stress your experience with big data, automation, and reliability.

3.3.1 Describe modifying a billion rows in a production environment.
Discuss bulk update strategies, transactional safety, and downtime minimization. Example: “I’d use partitioned updates, batch processing, and monitor performance metrics to avoid locking.”

3.3.2 Design a data pipeline for hourly user analytics.
Explain ingestion, aggregation, storage, and reporting flows. Example: “I’d use streaming ingestion, windowed aggregations, and a dashboard endpoint for hourly metrics.”

3.3.3 Ensuring data quality within a complex ETL setup.
Highlight validation steps, error logging, and data reconciliation processes. Example: “I’d implement schema checks, anomaly detection, and periodic audits to maintain integrity.”

3.3.4 Design and describe key components of a RAG pipeline.
Outline retrieval, augmentation, and generation steps, plus monitoring and fallback mechanisms. Example: “I’d use a vector database for retrieval, a transformer for augmentation, and metrics for output quality.”

3.3.5 Write a query to compute the average time it takes for each user to respond to the previous system message.
Use window functions to align messages, calculate time differences, and aggregate by user. Example: “Partition by user, order by timestamp, and compute lag between system and user messages.”

3.4. Data Cleaning & Quality

Expect questions about your practical experience cleaning messy, inconsistent datasets and your strategies for ensuring reliable insights. Focus on profiling, automated checks, and communicating limitations.

3.4.1 Describing a real-world data cleaning and organization project.
Walk through steps for profiling, handling missing values, and documenting your cleaning process. Example: “I started with null profiling, applied imputation for MAR patterns, and logged all transformations for auditability.”

3.4.2 How would you approach improving the quality of airline data?
Discuss root cause analysis, validation, and remediation techniques. Example: “I’d profile for outliers, apply business rules, and automate quality dashboards.”

3.4.3 Ensuring data quality within a complex ETL setup.
Detail how you monitor, log, and resolve data issues in multi-step ETL pipelines. Example: “I’d set up error alerts, periodic audits, and reconciliation scripts between sources.”

3.4.4 Demystifying data for non-technical users through visualization and clear communication.
Explain using intuitive charts, clear legends, and storytelling. Example: “I use color-coded dashboards and annotate key trends with plain language.”

3.4.5 Making data-driven insights actionable for those without technical expertise.
Focus on simplifying metrics and relating findings to business goals. Example: “I translate statistical confidence into business risk and offer clear next steps.”

3.5. Communication & Stakeholder Management

These questions assess your ability to present complex insights, align cross-functional teams, and resolve misaligned expectations. Show how you tailor your approach to different audiences and drive consensus.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience.
Describe adjusting technical depth, using visuals, and focusing on actionable recommendations. Example: “I start with the business impact, then layer in supporting data as needed.”

3.5.2 Strategically resolving misaligned expectations with stakeholders for a successful project outcome.
Explain proactive communication, expectation setting, and compromise. Example: “I schedule regular check-ins, clarify requirements, and document decisions.”

3.5.3 Describing a data project and its challenges.
Share obstacles faced, solutions tried, and lessons learned. Example: “A major data source changed schema mid-project, so I built automated schema checks and updated ETL scripts.”

3.5.4 How would you analyze how the feature is performing?
Discuss setting KPIs, tracking metrics, and generating actionable insights. Example: “I’d define conversion goals, monitor engagement, and recommend product tweaks based on usage trends.”

3.5.5 Why do you want to work with us?
Connect your skills and interests to the company’s mission and data challenges. Example: “I’m excited about using my experience in scalable systems to help Attom unlock deeper insights from real estate data.”

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe a specific scenario where your analysis led to a concrete business outcome. Highlight the problem, your method, and the result.

3.6.2 Describe a challenging data project and how you handled it.
Share the obstacles, your approach to resolving them, and the impact on the project’s success.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your process for clarifying goals, communicating with stakeholders, and iterating on solutions.

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?
Provide an example of how you fostered collaboration and found common ground.

3.6.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Discuss the communication strategies you used to ensure alignment and understanding.

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?
Show how you balanced priorities, communicated trade-offs, and maintained project integrity.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Highlight your persuasion skills, use of evidence, and relationship building.

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

3.6.9 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Detail your approach to data validation and making informed decisions.

3.6.10 How have you balanced speed versus rigor when leadership needed a “directional” answer by tomorrow?
Share how you prioritized essential data cleaning and communicated uncertainty transparently.

4.1 Company-specific tips:

Gain a deep understanding of Attom Data Solutions’ core business in real estate data and analytics. Familiarize yourself with the types of property data Attom aggregates—such as deeds, mortgages, foreclosures, and neighborhood trends—and how this data is used by clients in real estate, mortgage, insurance, and government sectors. Demonstrating awareness of Attom’s mission to deliver actionable property insights will help you connect your technical solutions to real business impact.

Review recent product launches, data platform enhancements, and technology stacks mentioned in Attom’s engineering blog posts or press releases. Be prepared to discuss how you would contribute to their ongoing efforts to scale data ingestion and improve API reliability for diverse clients. Showing genuine interest in Attom’s data-driven innovation will set you apart.

Understand the challenges unique to handling large-scale property data, such as ensuring data quality, managing schema evolution, and integrating disparate data sources. Be ready to discuss how you would approach these challenges in the context of Attom’s real estate data platform, referencing relevant experience from your past projects.

4.2 Role-specific tips:

4.2.1 Practice designing scalable ETL pipelines for heterogeneous and high-volume data.
Focus on building modular data pipelines that can ingest, validate, and transform large volumes of property data from various sources. Prepare to discuss strategies for error handling, schema mapping, and monitoring to ensure reliability and extensibility as new data partners are onboarded.

4.2.2 Demonstrate expertise in database schema design for complex, evolving datasets.
Be ready to design normalized, efficient schemas for systems like ride-sharing apps, online retailers, or airlines. Show how you would use indexing, partitioning, and referential integrity to optimize for query performance and scalability. Relate your designs to Attom’s need for flexible, robust data storage across millions of property records.

4.2.3 Show proficiency in writing advanced SQL queries for analytics and reporting.
Practice queries involving window functions, aggregations, and ranking—such as calculating top departments by salary or average response times. Highlight your ability to troubleshoot ETL errors and extract actionable insights from messy or incomplete data.

4.2.4 Prepare to discuss system architecture and trade-offs in scalable, distributed environments.
Be ready to break down requirements for digital classroom services or data warehouses, explaining your choices in microservices, cloud storage, and modular design. Emphasize scalability, reliability, and maintainability, connecting your approach to Attom’s platform needs.

4.2.5 Illustrate your experience with data cleaning, quality assurance, and documentation.
Share concrete examples of projects where you profiled data, handled missing values, and automated quality checks. Discuss how you documented your cleaning process for auditability and communicated data limitations to both technical and non-technical audiences.

4.2.6 Highlight your ability to communicate complex technical concepts to stakeholders.
Prepare stories about presenting data insights to cross-functional teams, resolving misaligned expectations, and making technical recommendations accessible to non-technical users. Show how you adapt your communication style to drive consensus and support informed decision-making.

4.2.7 Reflect on your experience managing ambiguity and scope creep in data projects.
Have examples ready of how you clarified requirements, negotiated priorities, and kept projects on track when faced with unclear goals or shifting stakeholder demands. Demonstrate your ability to balance technical rigor with business needs.

4.2.8 Be ready to discuss real-world challenges in data engineering and how you overcame them.
Share detailed stories about modifying billions of rows in production, reconciling conflicting data sources, or building pipelines for hourly analytics. Focus on your problem-solving approach, lessons learned, and the impact of your solutions.

4.2.9 Connect your motivation and skills to Attom’s mission and future growth.
Articulate why you are excited to work at Attom, referencing your experience in scalable system design, data engineering, or real estate analytics. Show how your background aligns with Attom’s needs and your enthusiasm for driving innovation in property data solutions.

5.1 How hard is the Attom Data Solutions Software Engineer interview?
The Attom Data Solutions Software Engineer interview is challenging, especially for candidates who haven’t worked with large-scale data platforms or real estate analytics before. You’ll be expected to demonstrate expertise in system design, scalable ETL pipelines, database schema design, and communicating technical concepts to diverse stakeholders. The technical rounds are rigorous and require strong coding skills, architectural thinking, and practical experience with data engineering. If you’re prepared to discuss real-world projects and can clearly articulate your solutions, you’ll be well positioned to succeed.

5.2 How many interview rounds does Attom Data Solutions have for Software Engineer?
The process typically consists of 5 to 6 rounds:
1. Application & resume review
2. Recruiter screen
3. Technical/case/skills round
4. Behavioral interview
5. Final onsite (multiple back-to-back interviews)
6. Offer & negotiation
Each stage is designed to assess both technical depth and communication skills, with the onsite round providing a comprehensive evaluation.

5.3 Does Attom Data Solutions ask for take-home assignments for Software Engineer?
Take-home assignments are not a standard part of every Attom Data Solutions Software Engineer interview, but some candidates may be asked to complete a technical case study or a coding exercise that simulates real-world data engineering challenges. This is typically used to assess your approach to designing scalable solutions and your ability to write clean, maintainable code.

5.4 What skills are required for the Attom Data Solutions Software Engineer?
Key skills include:
- Advanced coding in languages such as Python, Java, or C#
- Expertise in database schema design and query optimization (SQL, NoSQL)
- Building and maintaining scalable ETL pipelines
- System architecture for large-scale data platforms
- Data cleaning, quality assurance, and documentation
- Stakeholder communication and cross-functional teamwork
- Problem-solving in ambiguous or evolving environments
Experience with cloud technologies and real estate data is a plus.

5.5 How long does the Attom Data Solutions Software Engineer hiring process take?
The typical timeline is 3–4 weeks from initial application to final offer. Fast-track candidates may progress in 2–3 weeks, while the standard pace allows for a week or more between rounds to accommodate scheduling and feedback. Onsite interviews are usually consolidated into one day.

5.6 What types of questions are asked in the Attom Data Solutions Software Engineer interview?
Expect questions in these categories:
- System design (e.g., scalable data platforms, ETL pipelines)
- Database modeling and query writing
- Data engineering and scalability challenges
- Data cleaning and quality assurance
- Communication and stakeholder management
- Behavioral scenarios involving teamwork, ambiguity, and project challenges
These questions are tailored to Attom’s real estate data context and require both technical depth and clear communication.

5.7 Does Attom Data Solutions give feedback after the Software Engineer interview?
Attom Data Solutions generally provides high-level feedback through recruiters, especially after onsite rounds. Detailed technical feedback may be limited, but you can expect to hear about your strengths and areas for improvement if you request it.

5.8 What is the acceptance rate for Attom Data Solutions Software Engineer applicants?
While exact numbers aren’t public, the acceptance rate is competitive—estimated to be around 3–6% for qualified applicants. Candidates with strong data engineering backgrounds and clear communication skills have a distinct advantage.

5.9 Does Attom Data Solutions hire remote Software Engineer positions?
Yes, Attom Data Solutions offers remote opportunities for Software Engineers, though some roles may require occasional in-person collaboration or visits to the office. Flexibility depends on the team and project needs, but remote work is supported for many engineering positions.

• Related guides:
  • Attom Data Solutions interview questions
  • Software Engineer interview guide
  • Top data engineering interview tips

Ready to Ace Your Interview?

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

With resources like the Attom Data Solutions Software 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. Dive into topics such as scalable ETL pipeline design, database schema modeling, system architecture for large-scale real estate data platforms, and stakeholder communication strategies—all directly relevant to the challenges you’ll face at Attom.

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!