How to Prepare for a Data Modeling Interview in Three Simple Steps

Landing a data engineering role often hinges on how well you perform in the data modeling interview. It’s a critical step, but many candidates stumble, not because they lack the technical skills, but because they rush into solutions without truly understanding the problem. In this guide, we’ll reveal a three-step framework that will help you stand out from the crowd and ace your next data modeling interview.

Why Are Data Modeling Interviews So Challenging?

Data modeling interviews aren’t just about finding the “right” answer. They’re designed to evaluate your critical thinking and analytical abilities. Interviewers want to see how you approach a problem, how you ask questions, and how you connect your solutions to real-world business needs. In short, they care more about how you think than what you know.

Framework 0: Dogfooding – Become a User of Your Own Product

Before diving into any data modeling question, immerse yourself in the product you’re working with. This is known as “dogfooding,” or eating your own product.

Imagine you’re interviewing for DoorDash. Spend time using the app extensively. Tap every button. Explore every feature. Visualize the data that’s being collected and how it flows through the system. This firsthand experience will give you valuable insights and help you anticipate the interviewer’s questions.

If the product isn’t consumer-facing, find a proxy or do some online research to understand the user experience.

Think about Spotify. How many ways can you define “engagement?” Listening to more songs? Listening for longer periods? Opening the app regularly? Creating playlists? Each of these actions represents a different facet of user engagement, and understanding them is crucial for crafting an effective data model.

The #1 Mistake: Jumping Straight to Solutions

One of the biggest mistakes candidates make is immediately trying to create tables without first clarifying the problem. This approach is problematic for several reasons.

Interviewers often won’t interrupt you, even if you’re heading down the wrong path. They’ll let you ramble for 30 minutes, only to tell you at the end that you missed the point entirely. Don’t waste valuable time by rushing into a solution without a clear understanding of the problem.

Framework #1: Ask a TON of Questions

Asking questions is the single most important thing you can do in a data modeling interview. In fact, the ambiguity of the question is often intentional. Interviewers want to see how you think, how you analyze the problem, and what questions you ask to gain clarity.

Aim to ask at least 10 questions to fully understand the problem. Don’t be afraid to dig deep and challenge assumptions.

Here are some example questions you could ask, using the DoorDash example:

• What does “engagement” mean specifically?
• What time frame are we focused on?
• Which part of the app are we considering?
• Is this only in the US?
• Are we talking about mobile vs. desktop? Android vs. iOS?
• Are we seeing a dip in conversions because of the holidays?
• Are we seeing a lot of people churn?

Focus on asking business-oriented questions that get to the heart of the problem.

Why Asking Questions Is a “Muscle”

Asking clarifying questions is a skill that needs to be developed. It’s like a muscle that gets stronger with practice. Most people struggle with this step because their brains aren’t used to formulating these types of questions. But with practice, you can train yourself to ask the right questions and uncover the key insights you need to succeed.

Framework #2: The Three-Word Step

This framework guides the problem-solving process:

Step 1: What’s the Goal?

After asking clarifying questions, define the specific goal the interviewer is looking to achieve. What problem are they trying to solve?

For example, maybe the goal is to increase the number of restaurants a user searches per day.

Step 2: Define the Metric

Quantify the goal with a clear, measurable metric. Metrics provide a concrete way to track progress and measure success.

In our DoorDash example, the metric could be increasing the average number of restaurants searched per user per day from two to four.

Step 3: Create the Tables

After defining the goal and metric, create the necessary fact and dimension tables to support your analysis. Your tables should be designed to help you track the defined metric and answer the initial question.

Real-World Application: Connecting Goals to Key Results

Think about how goals are set in a real-world work environment. Companies often use objectives and key results (OKRs) to define their goals and measure progress. Notice that goals are always tied to specific, measurable numbers. Your data modeling process should mirror this approach, with a clear focus on defining and tracking key metrics.

Understanding Fact and Dimension Tables

Fact tables store quantitative data about events, while dimension tables store descriptive attributes related to the facts. Together, these tables are used to analyze data and answer business questions. If you need to study these topics further, there are many resources online to help you learn more about fact and dimension tables.

Framework #3: Bonus – Tie Back to the Question

This ensures that your data model actually addresses the problem at hand. Candidates sometimes create tables based on past experiences, but they don’t always align with the specific question being asked.

Imagine you’re creating tables to sell more food when the actual goal is to increase restaurant searches. In this case, your tables wouldn’t be aligned with the defined metric, and you’d likely lose points.

Why Ambiguity Is Your Friend

Interviewers intentionally make the question ambiguous to assess your thinking process. They want to see how you analyze the problem, how you ask questions, and how you approach the solution. Embrace the ambiguity and use it as an opportunity to demonstrate your critical thinking skills.

The Most Important Takeaway

Asking a ton of questions is the most crucial skill to master for the data modeling interview. It’s the foundation upon which everything else is built.

Actionable Steps to Prepare

Start practicing asking clarifying questions with sample data modeling problems. Use the DoorDash example provided in this guide as a starting point. Seek feedback on the types of questions you ask and how you can improve your approach.

Wrapp Up

By mastering the three-step framework, you’ll be well-equipped to tackle any data modeling interview question that comes your way. Remember to ask questions, define your metrics, and always tie your solutions back to the original problem. With practice and preparation, you can build the confidence you need to land your dream data engineering role.

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