10 System Design Interview Tips That Got Me to $500K

Original Description

⬇️ Click here to get our free ebook on acing the system design interview ⬇️ https://chrisbde942.clickfunnels.com/e-book-optin7a5d08vb

Learn how to ace the system design interview for data engineering roles from someone who went from $50K to $500K in under 5 years. This video breaks down the top 10 interview tips from our latest book, The System Design Cheat Code, so you can prepare strategically and impress interviewers at top tech companies.

Most candidates make critical mistakes: they jump straight to solutions without clarifying requirements, they can't explain trade-offs between different tools, and they fail to demonstrate awareness of real-world production concerns like failure recovery, data quality monitoring, and scalability.

What you'll learn:

1. Why clarifying requirements is the #1 thing interviewers look for (and how to ask 10-15 smart questions)

2. The 6-part framework for structuring any system design problem

3.How to frame every design decision in terms of trade-offs (Snowflake vs Redshift, batch vs streaming, etc.)

4. Why demonstrating awareness of failure recovery impresses senior engineers

5. The difference between incremental and full loads (and when to use each)

6. How to talk about data quality, observability, and monitoring before the interviewer asks

7. When security and governance matter (and when they don't)

8. The secret to communicating complex architectures clearly using a numbered talk track

Whether you're interviewing at FAANG companies, startups, or mid-size tech firms, these principles apply universally. This isn't about memorizing tools—it's about demonstrating you think like a senior engineer who understands business context, anticipates problems, and makes informed trade-offs.

Want the full System Design Cheat Code book for free? Click the link in the description.

TIMESTAMPS:

00:00:00 Introduction - $50K to $500K

00:00:32 Tip #1: Clarify Requirements First

00:01:42 Why Engineers Jump to Conclusions

00:02:34 Ask 5-15 Clarifying Questions

00:03:28 Tip #2: Six-Part Breakdown Framework

00:04:40 Orchestration and CI/CD

00:05:36 Tip #3: Frame Designs as Trade-Offs

00:06:42 The House Design Analogy

00:08:06 How to Explain Trade-Offs

00:09:00 Tip #4: Reliability and Recovery

00:10:27 Failure Recovery Examples

00:11:02 Tip #5: Incremental vs Full Loads

00:12:13 Handling Scale - The Taylor Swift Example

00:14:06 Tip #6: Detecting Deltas

00:15:23 Idempotency in Data Pipelines

00:15:52 Tip #7: Talk Observability Early

00:16:32 Why Data Quality Matters More Than ML

00:17:23 Monitoring and Alerting Setup

00:19:11 Tip #8: Security and Governance

00:20:41 When to Consider Privacy Data

00:21:37 Tip #9: Communicate Scalability Concerns

00:22:09 Tip #10: Use a Numbered Talk Track

00:23:35 Example: Walking Through Architecture

00:24:23 Get the Free System Design Book

00:25:03 Next Steps - Watch Full Book Intro

If you’re new to my channel, my name is Christopher Garzon. I run the top Data Engineering Academy in the country, where we help students transition into data engineering from other data professions to increase their compensation.

How I got here…

At 18 years old, I started at Boston College.

At 20, I was sneaking into graduate-level classes to take machine learning and data science courses.

At 21, I invested in a data science course from a mentor and wired him $3,000 without ever meeting him.

At 22, I landed my first job as a data analyst at Amazon, making $60,000 per year.

At 24, I became a data engineer at Amazon, increasing my salary to $100,000 and started angel investing in a couple of data companies.

At 25, I moved to a startup as a data engineer and doubled my income to $200,000 per year.

At 26, I was making about $350,000 at Lyft.

At 27, Lyft stocks went up, and my total compensation reached around $450,000. That same year, I launched the Data Engineering Academy.

For the last two and a half years, I’ve been running the Data Engineering Academy full-time, helping thousands of people transition into data engineering and significantly increase their earning potential.

To all the data professionals grinding—your journey is still being written. The bigger the obstacles, the greater the story.

Remember, don’t settle for your next job. Go for a better one.

Chris

More Resources:

- Learn Snowflake in 2 Hours: https://youtu.be/mP3QbYURT9k?si=722dm-5hvWFeOqnB

- How to Ace the Data Modeling Interview: https://youtu.be/YFVhC3SK0A0?si=YGLS3wjYHhdwYpVA

- Don't Get Replaced by AI: https://youtu.be/hMZrHIJshFU?si=aX7NeTxohBLHNZ3j

⬇️ Click here to get our free ebook on acing the system design interview ⬇️ https://chrisbde942.clickfunnels.com/e-book-optin7a5d08vb