To succeed, you must understand exactly what the engineering team at DICK'S Sporting Goods is looking for. The evaluation focuses heavily on practical execution, architectural understanding, and team collaboration.
Timed Technical Execution (Python & SQL)
This area is critical because it acts as the primary gatekeeper in the process. You will be evaluated on your ability to quickly and accurately solve data manipulation and querying problems under a strict time limit. Strong performance means not just finding a solution, but writing clean, optimized code that handles edge cases effectively.
Be ready to go over:
- Advanced SQL Querying – Window functions, complex joins, CTEs, and query optimization techniques.
- Python Data Manipulation – Using pandas, dictionaries, and core Python data structures to clean, transform, and aggregate datasets.
- Algorithmic Efficiency – Understanding time and space complexity to ensure your scripts can handle large data volumes without timing out.
- Advanced concepts (less common) – Dynamic SQL generation, handling JSON/XML parsing within SQL, and building custom Python generators for streaming data.
Example questions or scenarios:
- "Write a SQL query to find the top 3 selling products per category over the last 30 days using window functions."
- "Given a messy CSV of customer transactions, write a Python script to clean the data, remove duplicates, and aggregate total spend by user."
- "Optimize a provided SQL query that is currently running too slowly due to nested subqueries and inefficient joins."
Technical Panel & Code Review
This phase tests your ability to collaborate and defend your technical decisions. It is evaluated by having you walk through your previous assessment code or solve new problems live with the team. Strong performance looks like a confident, open dialogue where you can explain your reasoning, acknowledge potential flaws, and suggest alternative approaches.
Be ready to go over:
- Code Walkthroughs – Explaining step-by-step what your code does and why you chose a specific approach.
- Data Pipeline Design – Discussing how you would take your standalone script and deploy it as an automated, robust ETL pipeline.
- Error Handling and Logging – Explaining how you ensure data quality and handle pipeline failures in a production environment.
- Advanced concepts (less common) – CI/CD practices for data engineering, infrastructure as code (Terraform), and containerization (Docker/Kubernetes).
Example questions or scenarios:
- "Looking at the Python script you submitted, how would you modify it if the input dataset was 100 times larger?"
- "Walk us through how you would schedule and monitor this SQL transformation in a production environment."
- "Tell us about a time you discovered a data quality issue in production. How did you troubleshoot and resolve it?"
Behavioral and Culture Fit
Technical skills get you through the door, but culture fit secures the offer. This area evaluates your leadership, ownership, and ability to thrive in a fast-paced retail tech environment. Strong performance involves telling structured, impactful stories that highlight your collaboration, adaptability, and focus on business outcomes.
Be ready to go over:
- Cross-Functional Collaboration – How you work with product managers, data scientists, and business stakeholders to define data requirements.
- Handling Ambiguity – Navigating projects where requirements are unclear or frequently changing.
- Ownership and Impact – Demonstrating how your specific engineering contributions drove measurable improvements or cost savings.
- Advanced concepts (less common) – Mentoring junior engineers, leading technical migrations, or driving data governance initiatives.
Example questions or scenarios:
- "Describe a time when you had to explain a complex technical data issue to a non-technical stakeholder."
- "Tell me about a project that failed or missed a deadline. What did you learn from it?"
- "How do you prioritize your work when dealing with multiple urgent data requests from different teams?"
