Data Engineer II Interview Experience at Grubhub

I recently interviewed for the Data Engineer II position at Grubhub, and I’d like to share my experience with the interview process to help others who are preparing for similar roles. As a Data Engineer at Grubhub, you will be expected to work on optimizing data pipelines, transforming raw data into valuable insights, and collaborating with teams to support various business operations, particularly focusing on growth and marketing.

Overview of the Role

The Data Engineer II position at Grubhub involves designing and optimizing data infrastructure, building scalable data pipelines, and enabling data-driven decision-making processes. As a Data Engineer, you’ll be working with large volumes of data, utilizing technologies like Python, SQL, AWS, and Spark to process and analyze data. You’ll collaborate closely with data scientists, analysts, and product teams to help scale Grubhub’s platform and improve the customer experience.

Interview Process

The interview process was structured and included several rounds that tested both my technical knowledge and my ability to collaborate effectively with cross-functional teams.

1. Initial Screening (HR Interview)

Overview: The first step involved a conversation with an HR recruiter. This interview was mostly about understanding my background, career goals, and why I wanted to work at Grubhub. The recruiter also provided a detailed description of the job responsibilities and asked about my experience with data engineering.

Example Question:

• “Why are you interested in working at Grubhub, and what about the Data Engineer role excites you?“

2. Technical Phone Interview

Overview: After the HR interview, I had a technical phone interview with a senior data engineer. This round focused on assessing my technical expertise in SQL, Python, and data pipeline design. The interviewer was particularly interested in how I would approach real-world data engineering challenges at Grubhub.

Key Areas Covered:

• SQL: I was asked to write complex SQL queries to solve business problems like analyzing order data or segmenting customer behavior. This included aggregation, JOINs, and using advanced SQL functions like window functions.
• Data Pipeline Design: I was asked about my experience in designing ETL pipelines, specifically how I would manage large datasets, ensure data quality, and optimize the pipeline for scalability.
• Python: Questions focused on using Python for data processing, including libraries like Pandas and PySpark.

Example Question:

• “Write a SQL query to find the top 5 customers by total order value in the last 30 days.”

3. Onsite Interview (Multiple Rounds)

The onsite interview was split into multiple rounds, each assessing different technical and interpersonal aspects of the role.

Round 1 - SQL and Data Modeling

In this round, I was given a scenario where I had to design a database schema to support a new feature at Grubhub. This required knowledge of relational databases, normalization, and data integrity.

Example Question:

• “Design a database schema to track customer orders, including product details and payment status. What tables would you create, and how would you ensure that data is consistent and accurate?”

Round 2 - Data Engineering and Problem Solving

This round focused on my ability to design scalable data pipelines. The interviewer wanted to know how I would take raw data, process it efficiently, and make it available for analysis or downstream processes.

Example Question:

• “How would you design a pipeline to aggregate delivery data from multiple sources (e.g., customer orders, restaurant logs, delivery drivers) and make it available for real-time analytics?”

Round 3 - System Design and Architecture

I was asked to design a system that could handle Grubhub’s growing data needs. This round focused on scaling data solutions to handle increasing data volumes and ensuring performance.

Example Question:

• “Design a real-time data processing system to track delivery statuses across the entire fleet. How would you handle high volumes of data, and what tools would you use for real-time analytics?”

Round 4 - Behavioral and Collaboration

This round assessed how I work in teams, prioritize tasks, and handle conflict. It also focused on my ability to explain technical concepts to non-technical stakeholders and collaborate with different departments.

Example Question:

• “Tell us about a time when you had to work with a cross-functional team to solve a data-related problem. How did you manage communication, and what was the outcome?“

4. Final Round (Cultural Fit and Vision Alignment)

Overview: In the final round, I met with senior leadership to discuss Grubhub’s mission, how my work would align with the company’s long-term goals, and how I see myself contributing to the company.

Example Question:

• “Where do you see the future of data engineering in the food delivery industry, and how would you contribute to that future at Grubhub?”

Key Skills and Experience

To succeed in this role, the following skills are essential:

• Strong SQL Skills: Advanced knowledge of SQL, including writing complex queries, optimizing performance, and working with large datasets.
• Data Pipeline Development: Experience in designing and implementing scalable ETL pipelines. Familiarity with tools like Apache Spark, Airflow, or similar technologies is a plus.
• Python and Data Processing: Proficiency in Python for data processing and automation. Familiarity with libraries like Pandas, NumPy, and PySpark is important.
• Data Warehousing: Understanding of data warehousing principles like star schemas, normalization, and dimensional modeling.
• Cloud Technologies: Familiarity with cloud platforms like AWS (e.g., S3, EMR, Redshift) to manage and scale data infrastructure.
• Communication and Collaboration: Ability to collaborate with product, marketing, and analytics teams to design data systems that meet business needs.

What to Expect

• Technical Depth: Expect to be tested on your SQL skills, data modeling, and data pipeline design. Be prepared for complex SQL queries and data transformation challenges.
• System Design: Be ready to discuss how you would design large-scale, efficient data systems that can scale with Grubhub’s growing needs.
• Behavioral Questions: Prepare to discuss how you’ve worked in cross-functional teams, handled data challenges, and communicated technical details to non-technical teams.

Final Tips

• Master SQL: Be ready to solve complex SQL problems, particularly those involving joins, aggregations, and optimization.
• Understand Data Pipelines: Be prepared to discuss how you would design, implement, and optimize data pipelines for large-scale systems.
• Learn About Grubhub’s Data Needs: Research how Grubhub uses data for marketing, operations, and customer engagement to tailor your answers to the company’s specific needs.
• Show Leadership: Emphasize your ability to lead projects, collaborate across teams, and contribute to both technical and business goals.