At Robert Slack, data is the engine that drives transaction volume, agent productivity, and market expansion. As a Data Engineer, you will be responsible for building, optimizing, and maintaining the robust data pipelines that ingest massive amounts of real estate listings, customer interactions, and transaction histories. Your work directly impacts the company's proprietary systems, enabling agents to receive high-quality leads in real-time and allowing leadership to make critical business decisions based on accurate, up-to-the-minute market analytics.

The datasets you manage are complex, highly dynamic, and require sophisticated transformation logic to maintain data integrity. You will work on optimizing data flows from various external real estate databases, internal CRM platforms, and marketing channels. By ensuring that these data sources are seamlessly integrated into a centralized data warehouse, you empower downstream analytics, machine learning models, and customer-facing features to operate with maximum efficiency and reliability.

This role requires a unique blend of technical expertise, architectural foresight, and a deep understanding of cloud infrastructure. Joining the data team at Robert Slack means tackling challenging data scaling problems, designing fault-tolerant systems, and directly contributing to the technological edge that keeps the company at the forefront of the real estate industry.

To help you prepare effectively, we have compiled a representative list of questions based on real interview experiences for the Data Engineer position. These questions highlight key patterns in what the hiring team evaluates, ranging from foundational programming and database knowledge to cloud architecture and behavioral alignment.

SQL & Data Manipulation

These questions evaluate your ability to write clean, optimized queries and manipulate datasets efficiently.

• Write a query to find the top-performing real estate agents based on monthly transaction volume, handling ties appropriately.
• How do you perform complex string manipulations in SQL to clean and standardize messy address data?

Succeeding in the Robert Slack interview process requires a balanced preparation strategy. You must demonstrate not only deep technical competence but also the ability to communicate your architectural decisions clearly and align your work with business goals.

The hiring team evaluates candidates across several key dimensions:

Data Pipeline Craftsmanship – This represents your core technical capability to design, build, and maintain reliable data pipelines. Interviewers will assess your mastery of SQL, PySpark, and cloud-native tools, looking for your ability to write clean, efficient, and reusable code.

Analytical Problem-Solving – You will be evaluated on how you approach ambiguous data challenges. This includes your ability to identify edge cases, handle dirty or unstructured data, and design systems that degrade gracefully when unexpected issues arise.

Technical Communication – Because the interview process involves explaining your code—both in recorded formats and live panels—you must be able to articulate your technical choices clearly. You need to explain why you chose a specific tool, algorithm, or data structure over alternatives.

Collaborative Adaptability – The team values engineers who can work cross-functionally and adapt to changing requirements. You should be prepared to discuss how you collaborate with product managers, analysts, and business stakeholders to turn raw requirements into production-ready data assets.

The interview process for the Data Engineer position at Robert Slack is designed to thoroughly evaluate your technical skills, problem-solving methodologies, and communication style. The process typically spans several stages, moving from automated assessments to deep-dive technical discussions and final panel rounds.

Candidates generally begin with an automated screen, which frequently utilizes video-based platforms like HireVue. In this initial round, you will record video responses to behavioral and basic technical questions, and you may complete a short coding challenge. This stage is designed to assess your communication clarity and foundational coding skills before you move to more resource-intensive rounds.

For candidates who pass the initial screen, the process transitions to a practical evaluation, which may include a take-home data assignment or a direct coding test. This is followed by a technical review session where you will walk through your code with engineering team members. The final stage is a comprehensive loop interview, often consisting of a live coding session, a deep dive into AWS cloud architecture, and multiple behavioral interviews with hiring managers and team members.

The timeline above outlines the standard progression from your initial application to the final offer stage. Candidates should use this visualization to pace their preparation, ensuring they allocate sufficient time to practice both recorded communication and live system design. While the exact duration can vary based on scheduling, the overall structure remains highly consistent across engineering teams.

To excel in the Robert Slack interview, you must understand the specific competencies evaluated at each major stage of the process.

One-Way Video Assessments (HireVue)

The initial video assessment is a critical hurdle that requires a unique set of preparation strategies. Many candidates find this stage challenging because you are speaking directly to a screen without real-time feedback from a human interviewer.

Be ready to go over:

• Structured behavioral responses – Using the STAR (Situation, Task, Action, Result) method to answer questions concisely within the allotted time.
• On-camera coding explanations – Explaining your algorithmic logic clearly immediately after completing a short coding challenge.
• Time management – Balancing your preparation time (typically one minute) and your recording time (typically three minutes) effectively.

Example questions or scenarios:

• "Explain how you designed a data pipeline in a previous role, and describe the specific technologies you used to ensure its scalability."
• "Describe a situation where you had to learn a new data engineering tool quickly to complete a project. What steps did you take?"
• "Walk through the coding challenge you just completed, explaining your choice of data structures and the time complexity of your solution."

Core Data Engineering (SQL & PySpark)

This evaluation area focuses on your ability to manipulate data efficiently and write production-grade ETL code. You will face both live coding tasks and theoretical questions about distributed computing.

Be ready to go over:

• SQL optimization – Utilizing indexing, partitioning, and efficient join strategies to minimize query execution time on large datasets.
• Distributed data processing – Understanding how Spark distributes workloads across a cluster and managing partitions to avoid data skew.
• Data cleaning and transformation – Performing complex string manipulations, handling null values, and parsing nested data structures.
• Advanced concepts (less common) – Broadcast joins, custom User-Defined Functions (UDFs) optimization, and managing stateful streaming data.

Example questions or scenarios:

• "Write a PySpark script to read a directory of CSV files, clean the schema, and write the output to Parquet format partitioned by date."
• "How would you optimize a SQL query that joins a massive transaction table with a smaller lookup table, assuming the data is highly skewed?"
• "Explain the differences between client mode and cluster mode when deploying a Spark application."

Cloud Architecture & AWS

As a cloud-first engineering organization, Robert Slack heavily evaluates your ability to design and implement infrastructure within the AWS ecosystem.

Be ready to go over:

• Serverless data pipelines – Designing event-driven architectures using AWS Lambda, S3, and Athena.
• Data warehousing – Structuring tables, distribution keys, and sort keys in Amazon Redshift for high-performance analytical queries.
• Orchestration and cataloging – Using AWS Glue for metadata management and AWS Step Functions or Apache Airflow for pipeline orchestration.

Example questions or scenarios:

• "Design an end-to-end AWS architecture to ingest real-time clickstream data, process it, and make it queryable for business analysts within five minutes."
• "How would you set up a secure, automated process to copy data from an external vendor's S3 bucket into your own secure data lake?"
• "Describe how you would implement a disaster recovery plan for a critical Redshift data warehouse."

Take-Home Assignment & Technical Review

The take-home assignment evaluates your practical software engineering skills in a realistic, self-paced environment. The subsequent review session tests your ownership of the code you wrote.

Be ready to go over:

• Error handling and logging – Implementing robust validation checks and informative logging within your pipeline code.
• Data quality monitoring – Designing mechanisms to detect and isolate corrupt, incomplete, or anomalous data records.
• Design trade-offs – Explaining why you chose specific libraries, architectures, or data models during the development of your assignment.

Example questions or scenarios:

• "In your take-home code, how did you account for potential schema drift if the input source adds new columns in the future?"
• "Walk us through your strategy for unit testing the data transformations you implemented in your submission."
• "If the input dataset size scaled by a factor of one hundred, what modifications would you make to your code to ensure it still runs efficiently?"