How to Nail Your Data Engineering Interview (From the Galaxy Team)

Data engineering interviews are tough. They’re technical, structured, and often unpredictable. But if you know what to expect—and how to prepare—you can absolutely stand out. At Galaxy, we’ve helped thousands of engineers build and debug queries, practice system design, and master the data layer. So we pulled together everything we know about how to nail a data engineering interview in 2025.

Whether you’re applying to a Series A startup or a FAANG company, this guide will walk you through how to prep—and perform—at every stage.

🎯 What Interviewers Are Actually Looking For

Most data engineering interviews assess four things:

1. Technical competence (SQL, Python, pipelines, architecture)
2. Communication (Can you explain trade-offs and think aloud?)
3. Practical experience (Have you built and shipped?)
4. Scalability mindset (Are you solving for now or for scale?)

Interviewers want you to think like a systems builder and communicate like a teammate. Our advice below is focused on proving that you can do both.

🧠 How to Prepare: The Core Areas

1. Master SQL (seriously)

SQL is still the #1 signal in any data engineering interview. You’ll need to:

• Join multiple tables
• Use window functions like ROW_NUMBER() and LAG()
• Write nested subqueries and optimize for performance

Practice with real-world questions from platforms like LeetCode, StrataScratch, or inside Galaxy’s SQL editor (https://www.getgalaxy.io/features/sql-editor).

Make sure you understand not just how to write the query—but why it’s efficient.

2. Be Fluent in Python + Your Stack

Expect live coding exercises or take-homes. Common topics:

• Data transformations (think: string parsing, JSON flattening)
• File handling and APIs
• Writing unit-testable, modular code
• Using libraries like pandas, pySpark, or boto3

If you’re interviewing at companies using Airflow or dbt, brush up on their config patterns, Jinja templating, and modularity best practices.

We’ve also seen companies test Docker or basic infra concepts, especially for platform-focused roles.

3. Know Your Pipelines Cold

Be ready to walk through one of your past projects—end to end. You should be able to clearly explain:

• How the data is ingested (batch vs. streaming, frequency, tools used)
• How it’s transformed (SQL logic, Python scripts, dbt models)
• How it’s stored (data lake, warehouse, schema decisions)
• How it’s served (dashboards, APIs, downstream tools)

Even better? Add metrics: “This pipeline ingested 5M rows daily and reduced time-to-insight by 30%.”

4. Prepare for Data System Design

This is where great candidates really differentiate themselves.

Common prompts:

• “Design a logging system that scales to 10M events/hour.”
• “Build an architecture for a user-facing metrics dashboard.”
• “Set up a pipeline to backfill 5 years of data.”

Your interviewer wants to see trade-off thinking: batch vs. real-time, cloud costs vs. speed, simplicity vs. reliability.

We recommend whiteboarding or diagramming with tools like Excalidraw or Whimsical, and brushing up on dbt best practices, Airflow DAG structuring, and streaming concepts like Kafka partitions and checkpoints.

5. Show You Think Like a Teammate

Don’t overlook the behavioral portion. Some advice:

• Have 2–3 strong stories prepared (ideally ones where you solved real data problems)
• Use the STAR format (Situation, Task, Action, Result)
• Talk about collaboration with PMs, analysts, and data scientists
• Mention tools/processes you helped improve (e.g., “I standardized DAG logging across teams”)

Even technical interviewers want to know you’re someone they’d enjoy working with.

🔧 What to Bring Up (Even If They Don’t Ask)

Great candidates volunteer the stuff they know hiring teams care about. Try to organically mention:

• Data quality enforcement (unit tests, assertions, CI checks)
• Monitoring and alerting (Grafana, Sentry, custom Slack alerts)
• Documentation (dbt docs, Notion wikis, self-serve dashboards)
• Prioritization under constraints (technical debt, MVP-first mindset)

This shows maturity—you're not just writing code, you're thinking long-term.

💬 Example Questions to Expect

Here’s a quick cheat sheet:

SQL / Python

• “Find the top 3 most active users by month.”
• “Flatten a nested dictionary from a JSON API.”
• “Detect anomalies in daily event volumes.”

System Design

• “Design a clickstream ingestion pipeline.”
• “How would you scale dbt to hundreds of models and developers?”
• “Create a cost-efficient storage solution for 10 years of logs.”

Behavioral

• “Tell me about a time a pipeline broke in prod.”
• “How do you handle conflicting stakeholder needs?”
• “What’s the hardest thing you’ve debugged?”

🚀 Final Interview Tips from Galaxy

1. Use real examples. Show that you’ve been in the weeds. Don’t just name tools—explain where you used them and why.
2. Sketch things out. Even if you’re on Zoom, use a digital whiteboard or sketchpad to talk through architecture.
3. Clarify before you solve. Interviewers want collaboration. Ask clarifying questions before jumping into code.
4. Over-communicate tradeoffs. In system design, explain not just what you’d do, but what you’d choose not to do—and why.
5. Follow up. After the interview, send a thoughtful note summarizing the problem and how you’d approach it further. It shows initiative.

✨ Bonus: Practice With Tools Like Galaxy

Galaxy is a modern SQL editor built for data engineers, with AI-assisted query generation, schema exploration, and live documentation. You can:

• Practice writing and debugging queries
• Collaborate with peers or mentors
• Explore mock schemas like ecommerce or SaaS data
• Chat with your database to explain queries or refactor them

Try it for free here.

🔍 Recap: How to Nail Your Data Engineering Interview

AreaFocusSQL & PythonCore technical barData PipelinesEnd-to-end storytellingSystem DesignScalable architecture + tradeoffsBehavioralCollaboration and resilienceExtra SignalsMonitoring, testing, documentation

💡 Good Luck!

Data engineering interviews can be daunting—but with prep, practice, and real-world thinking, you can crush them.

If you’re prepping and want to explore tooling, check out our SQL workspace here, or read more of our interview resources here.

We’re rooting for you.
— The Galaxy Team ✌️

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