1. What is a Data Engineer at DICK'S Sporting Goods?

As a Data Engineer at DICK'S Sporting Goods, you are at the heart of the company’s mission to inspire and equip athletes through data-driven innovation. Retail is no longer just about brick-and-mortar stores; it is a complex, omnichannel ecosystem where personalized customer experiences, optimized supply chains, and real-time inventory management dictate success. Your role is critical in building the robust data foundations that power these strategic initiatives.

You will design, build, and maintain the scalable data pipelines that process massive volumes of retail and customer data. By transforming raw data into actionable insights, you directly empower data scientists, business analysts, and product teams to make decisions that impact millions of customers. Whether you are optimizing pricing models, enhancing the ScoreCard loyalty program, or streamlining warehouse logistics, your engineering work has a tangible, immediate impact on the business.

Expect to work in a fast-paced, collaborative environment where technical excellence meets retail strategy. DICK'S Sporting Goods values engineers who not only write clean, efficient code but also understand the broader business context. This role offers the perfect blend of technical complexity and strategic influence, challenging you to build resilient systems that scale with the company's continuous growth.

2. Common Interview Questions

While the exact questions will vary based on the specific team and your interviewer, reviewing common patterns will help you prepare effectively. The goal is to understand the underlying concepts being tested rather than memorizing answers.

SQL and Database Concepts

This category tests your ability to extract, manipulate, and aggregate data efficiently using SQL. Interviewers look for your grasp of advanced functions and query optimization.

• Write a query to calculate the rolling 7-day average of sales for each store location.
• How do you optimize a SQL query that is scanning a massive table and running too slowly?
• Explain the difference between a RANK(), DENSE_RANK(), and ROW_NUMBER() window function.
• Write a query to find customers who made a purchase in 2023 but did not return in 2024.
• How would you design a schema to track real-time inventory changes across multiple warehouses?

Python and Pipeline Engineering

These questions evaluate your coding proficiency, your understanding of data structures, and your ability to build resilient data processing scripts.

• Write a Python function to parse a nested JSON payload of customer orders and flatten it into a tabular format.
• How do you handle missing or malformed data when building an automated ETL pipeline?
• Explain how you would use pandas to merge two large datasets that do not fit entirely into memory.
• What strategies do you use for error handling and logging in your Python data scripts?
• Write a Python script to interact with a REST API, paginate through the results, and store the output.

System Design and Architecture

This category assesses your ability to design scalable, end-to-end data systems and make intelligent architectural trade-offs.

• Design a data pipeline to ingest real-time clickstream data from our e-commerce website and make it available for analytics within 5 minutes.
• When would you choose an ELT approach over a traditional ETL approach?
• Walk me through your preferred tech stack for building a modern data warehouse from scratch.
• How do you ensure data idempotency in your pipelines?
• Describe how you would migrate a legacy on-premise database to a cloud-based data warehouse with minimal downtime.

Behavioral and Leadership

These questions gauge your cultural fit, your problem-solving resilience, and your ability to work cross-functionally within DICK'S Sporting Goods.

• Tell me about a time you had to push back on a stakeholder's data request because it wasn't feasible or scalable.
• Describe a situation where you had to learn a new technology or tool very quickly to deliver a project.
• Walk me through the most complex data engineering problem you have solved in your career so far.
• Tell me about a time you identified a process improvement in your team's workflow and drove the change.
• How do you ensure alignment and clear communication when working with remote or distributed teams?

3. Getting Ready for Your Interviews

Thorough preparation is the key to navigating the interview process with confidence. DICK'S Sporting Goods evaluates candidates across a balanced spectrum of technical capability and collaborative fit. Focus your preparation on the following key criteria:

Technical Proficiency (Python & SQL) – This is the bedrock of your evaluation. Interviewers will assess your ability to write efficient, production-ready code under time constraints. You can demonstrate strength here by practicing complex SQL queries, mastering data manipulation in Python, and understanding how to optimize code for performance.

Problem-Solving and System Design – Beyond writing code, you will be evaluated on how you approach data architecture challenges. Interviewers want to see how you design ETL/ELT pipelines, handle data quality issues, and scale systems. You can excel by clearly articulating your thought process, discussing trade-offs, and showing a deep understanding of data modeling principles.

Communication and Code Review – Data engineering is highly collaborative. You will be evaluated on your ability to explain technical decisions to peers and stakeholders. Demonstrating strength in this area means being receptive to feedback during technical panels, clearly explaining the "why" behind your code, and showing how you work within a team environment.

Culture Fit and Adaptability – DICK'S Sporting Goods looks for candidates who are passionate, resilient, and adaptable. Interviewers will gauge how you navigate ambiguity and handle fast-paced delivery cycles. You can prove your fit by sharing examples of past projects where you took ownership, overcame obstacles, and delivered measurable business value.

4. Interview Process Overview

The interview process for a Data Engineer at DICK'S Sporting Goods is structured to be thorough yet efficient, typically concluding within a three-week timeframe. Your journey usually begins with a recruiter prescreen, followed quickly by a critical at-home coding assessment. This timed test heavily focuses on your practical proficiency in Python and SQL, setting a baseline for your technical capabilities.

If you pass the initial assessment, you will move to the core interview stages. This usually features a comprehensive technical panel interview lasting roughly 90 minutes. During this panel, you will review your initial coding assessment with current engineers, discuss your technical choices, and dive deeper into data engineering concepts. The process is collaborative; the team wants to see how you think on your feet and how you respond to code-review-style questions.

The final stage is typically a behavioral and fit interview with the hiring manager, lasting about 45 minutes. This conversation focuses on your past experiences, your approach to teamwork, and your alignment with the company's core values. While the process is generally straightforward and friendly, the pacing can be quick, and proactive communication with your recruiter is highly recommended to stay updated on your status.

This visual timeline outlines the typical progression from the initial recruiter screen through the technical assessments and final hiring manager interviews. Use this to structure your preparation timeline, focusing heavily on timed coding practice early on, and shifting toward architectural discussions and behavioral storytelling as you advance. Keep in mind that specific team requirements or locations may introduce slight variations, such as a group behavioral interview.

5. Deep Dive into Evaluation Areas

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?"

6. Key Responsibilities

As a Data Engineer at DICK'S Sporting Goods, your day-to-day work revolves around building and maintaining the data infrastructure that supports the entire retail operation. You will be responsible for designing, developing, and deploying scalable ETL/ELT pipelines that ingest data from various sources—including point-of-sale systems, e-commerce platforms, and supply chain databases—into a centralized data lake or warehouse. Ensuring data accuracy, reliability, and timeliness is your primary deliverable.

Collaboration is a massive part of your daily routine. You will work closely with Data Scientists who rely on your clean datasets to build machine learning models for inventory forecasting and personalized marketing. You will also partner with Business Intelligence analysts and Product Managers to understand their reporting needs, translating business requirements into robust data architectures. This requires a strong understanding of both technical systems and retail business logic.

You will frequently drive initiatives focused on system optimization and modernization. This includes refactoring legacy data pipelines for better performance, migrating on-premise solutions to cloud infrastructure, and implementing automated data quality checks. By continuously monitoring pipeline health and troubleshooting production issues, you ensure that the business has uninterrupted access to the critical data needed to serve athletes effectively.

7. Role Requirements & Qualifications

To be a highly competitive candidate for the Data Engineer role at DICK'S Sporting Goods, you need a solid blend of core engineering skills and practical experience with modern data stacks.

• Must-have skills – Exceptional proficiency in SQL and Python is non-negotiable. You must have hands-on experience building, testing, and deploying robust data pipelines (ETL/ELT). A strong understanding of relational databases, data warehousing concepts, and data modeling techniques is essential.
• Experience level – Typically, successful candidates bring 3 to 5+ years of dedicated data engineering or software engineering experience, preferably with a background in handling large-scale datasets in a fast-paced or retail environment.
• Soft skills – Strong communication skills are critical. You must be able to articulate technical trade-offs to peers and translate complex data concepts for business stakeholders. A proactive mindset, a high degree of ownership, and a collaborative spirit are highly valued.
• Nice-to-have skills – Experience with major cloud platforms (GCP, AWS, or Azure) and modern data tools (like Snowflake, dbt, or Databricks) will significantly differentiate you. Familiarity with big data processing frameworks (Spark, Kafka) and orchestration tools (Airflow) is a major plus.

8. Frequently Asked Questions

Q: How difficult is the technical assessment? The difficulty is generally considered average, but the strict time limit makes it challenging. You must be able to think quickly and write clean SQL and Python code without relying heavily on documentation. Practice timed exercises to build your speed.

Q: How long does the entire interview process take? The end-to-end process typically takes about three weeks from the initial recruiter screen to the final hiring manager interview. However, response times can occasionally lag, so do not hesitate to follow up politely with your recruiter.

Q: What differentiates a successful candidate at DICK'S Sporting Goods? Successful candidates do more than just pass the coding tests; they demonstrate a clear understanding of how their data pipelines impact the broader business. Showing an interest in retail analytics, customer experience, and supply chain optimization will set you apart.

Q: Should I expect to write code live during the panel interview? While the initial assessment is take-home/timed, the panel interview often involves a deep-dive review of the code you submitted. You may be asked to modify your code live, optimize it, or verbally walk through how you would scale it, so be prepared to defend your technical choices.

Q: What is the culture like within the data engineering team? The culture is highly collaborative, friendly, and fast-paced. Current employees note that team members are supportive during the interview process. The environment values continuous learning and proactive problem-solving to keep up with the dynamic nature of retail tech.

9. Other General Tips

• Practice Under Pressure: The initial Python and SQL assessment has a strict time limit. Simulate this environment by practicing on platforms like LeetCode or HackerRank with a timer running. Speed and accuracy are equally important here.

• Know Your Code Inside and Out: During the technical panel, you will likely review the code you wrote for the assessment. Be ready to explain your logic, discuss the time/space complexity of your solution, and suggest ways to improve or scale it.
• Master the STAR Method: For the behavioral round with the hiring manager, structure your answers using the Situation, Task, Action, Result framework. Focus heavily on the "Action" you took and the measurable "Result" of your work.

• Connect Tech to Retail: DICK'S Sporting Goods is fundamentally a retail business. Whenever possible, frame your technical answers in the context of retail challenges—such as inventory management, customer personalization, or omnichannel sales.
• Be Proactive with Communication: Some candidates have reported instances of slow feedback or ghosting after interviews. Take ownership of the process by sending polite follow-up emails to your recruiter if you haven't heard back within the expected timeframe.

10. Summary & Next Steps

Interviewing for a Data Engineer position at DICK'S Sporting Goods is an exciting opportunity to join a company that relies on data to drive the ultimate athlete experience. You will be challenged to demonstrate not only your raw coding speed in Python and SQL but also your architectural foresight and your ability to collaborate effectively with a team of skilled engineers.

This compensation data provides a baseline expectation for the role. Keep in mind that actual offers will vary based on your specific years of experience, your performance during the technical panels, and your geographic location. Use this information to anchor your expectations and prepare for future compensation discussions.

Focus your remaining preparation time on mastering timed coding exercises, practicing your code communication skills, and structuring your behavioral stories. Remember that the interviewers want you to succeed—they are looking for a teammate who can help them build scalable, impactful data solutions. For more targeted practice, explore the coding challenges and system design resources available on Dataford. Trust in your preparation, stay confident, and you will be well-positioned to ace your interviews.