Technical & Data Modeling

This category tests your hard skills in SQL and dbt.

• "Walk me through how you would design a schema to track inventory movement from a customer's home back to the warehouse."
• "What is the difference between a view, a table, and an incremental model in dbt? When would you use each?"
• "Write a query to find the top 10% of users by revenue for the last year."
• "How do you handle data quality issues in a production pipeline? Give an example of a test you would implement."
• "Explain how you would optimize a query that is taking too long to run on a large dataset."

Business Case & Problem Solving

These questions assess your ability to think like a business owner.

• "If we wanted to launch a new 'Unlimited' plan, what metrics would you track to ensure it is profitable?"
• "Inventory utilization is down this month. What hypotheses would you investigate?"
• "How would you determine if a specific designer brand is worth keeping in our portfolio?"
• "A stakeholder wants a dashboard that shows 'everything.' How do you handle this request?"

Behavioral & Culture

RTR values collaboration and ownership.

• "Tell me about a time you identified a data quality issue that no one else noticed. How did you fix it?"
• "Describe a situation where you had to explain a complex technical concept to a non-technical stakeholder."
• "How do you prioritize your work when you have requests from multiple different teams?"
• "Tell me about a time you had to learn a new tool or technology quickly to get a job done."

This timeline illustrates the typical flow from application to offer. Note that the "Technical Screen" often serves as a major filter; ensure you are comfortable with SQL window functions and dimensional modeling before this stage. The process is thorough because RTR looks for candidates who can immediately contribute to their dbt and Looker ecosystems.

Deep Dive into Evaluation Areas

SQL and Data Manipulation

This is the foundation of the role. You will be expected to write clean, efficient, and optimized SQL. RTR deals with large-scale datasets involving subscription lifecycles and event data (e.g., Heap).

Be ready to go over:

• Advanced Joins and Aggregations – Handling complex relationships between inventory, users, and subscriptions.
• Window Functions – Calculating running totals, ranking user engagement, or analyzing session trends.
• Query Optimization – Writing queries that are performant in a cloud warehouse environment like Snowflake or BigQuery.
• Data Cleaning – Handling NULLs, duplicates, and messy raw data from various sources.

Example questions or scenarios:

• "Write a query to calculate the month-over-month retention rate for new subscribers."
• "How would you identify the top 3 most rented items per category for each region?"
• "Given a table of user events, find the average time between a user's first rental and their second rental."

Analytics Engineering & dbt

Because RTR is building a dedicated Analytics Engineering function, this area is critical. You need to show that you treat data as code.

Be ready to go over:

• Data Modeling – Designing Star Schemas, defining facts and dimensions, and normalizing/denormalizing data appropriately.
• dbt Fundamentals – Understanding ref(), incremental models, snapshots, and the importance of DAGs (Directed Acyclic Graphs).
• Quality & Testing – Implementing data tests (unique, not null, referential integrity) to ensure reliability.
• Advanced concepts (less common) – Jinja templating in dbt, macro creation, and orchestration tools like Prefect.

Example questions or scenarios:

• "How would you refactor a massive, 1000-line SQL query into a modular dbt project?"
• "Explain your strategy for handling slowly changing dimensions (SCDs) for our inventory catalog."
• "How do you manage dependencies between models to ensure data freshness?"

Business Intelligence & Visualization

Your output must be consumable by the business. Proficiency in Looker is a significant advantage, as it is the primary BI tool at RTR.

Be ready to go over:

• LookML – Defining dimensions, measures, and explores in Looker’s modeling language.
• Dashboard Design – Creating intuitive visualizations that answer specific business questions without overwhelming the user.
• Self-Service Enablement – Designing models that allow stakeholders to answer their own questions.

Example questions or scenarios:

• "Design a dashboard for the Logistics team to monitor warehouse turnaround time."
• "A stakeholder says a metric looks 'wrong' on a dashboard. How do you investigate and resolve this?"
• "How do you balance granular data access with dashboard performance?"

Product Sense & Metrics

You must understand the business behind the data. RTR operates on a subscription model, which is distinct from traditional retail.

Be ready to go over:

• Subscription Metrics – Churn, Retention, CAC (Customer Acquisition Cost), LTV (Lifetime Value), and ARPU (Average Revenue Per User).
• Inventory Metrics – Utilization rates, turn rates, and reverse logistics efficiency.
• A/B Testing – Designing experiments to test new features or pricing tiers.

Example questions or scenarios:

• "We are seeing a dip in subscription renewals. How would you investigate the root cause?"
• "How would you measure the success of a new 'closet swap' feature?"
• "Define a metric to track the 'health' of our reverse logistics operations."