The Data Engineer Bootcamp - Learn From FAANG Engineers

The Data Engineer Bootcamp

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Prep for schema design, data modeling, and architecture design real life scenarios. Practice SQL and Python problems, and find what what FAANG companies really do on a day to day basis. Real world case studies to demonstrate the importance of designing scalable AWS infrastructure - learn about everything from storage to compute to data warehousing.

 YOU WILL LEARN

 SQL Questions

Resources for learning SQL + questions and standard tools used during the job. Learn how to answer SQL questions on a job. Hint: getting the answer right is not the most important factor.

Python Questions

How to best prepare for real life python scripting needs without needing to practice 100+ questions online – this is not a book on becoming a software engineer!

AWS Concepts

Learn the basics behind storage and compute and the tradeoffs between different services. How do you calculate the cost associated with building a robust infrastructure? Is there a way to account for unexpected spikes in incoming data?

Database Modeling

How do we choose between a fact or dim table? How about a Star schema? When to use 1 vs 5 primary keys? What is a foreign key? What are the most common mistakes when data modeling?

System Design

How to ensure the safety of our data while also optimizing for speed and cost. Is the data something we need in real time? How frequent is the data being pulled? Does it vary based on type of data?

Samples

Get personalized feedback for you answers to the questions below!

A) Data Modeling at Doordash: Let’s say you work for a delivery food company, XYZ. A customer can go to the XYZ app and place an order at their favorite restaurant for either pickup or delivery. Once that restaurant receives the order, they can choose to accept or deny the order. If accepted, then the XYZ app connects the restaurant with a nearby delivery man who comes, picks up the order, and delivers to food to the customer. Design a schema that takes this entire process into account.

B) SQL: We have two datasets- a list of users with a grubhub membership and a list of users with a doordash membership. Create a query to see what percent of users are in grubhub, doordash, and both.

C) System Design At Spotify: You work at a music streaming company called Spotify. You have access to all the data that any music streaming service would have. With every song that a user listens, the data scientists on the team can use Machine learning to learn quickly and give song Recommendations. However, they need the proper data and infrastructure to make their ML work. How would you design a Recommendation Engine?

Course Sections

1. Fact Tables

Learn about the different types of fact tables and real-life examples of companies that maintain them.

2. Dim Tables

Different types of dimension tables have their own benefit. Here we’ll discuss each one and see real life examples of companies which use them.

3. Database Modeling

Design a star schema from scratch and use your dim and fact knowledge from the previous sections to complete this exercise.

4.Metrics Via SQL

6 SQL exercises to create some of the most common metrics used by tech companies. Identify power users, create window functions, and join across multiple tables. SQL questions like the ones at Amazon and Netflix.

5.Data storage: Amazon S3

Most companies require a data lake. Learn about the fundamentals of storing data, and why how you organize your data matters.

6. Data Warehouse: AWS Redshift

Practical examples on how to build tables in redshift while optimizing for cost and storage. Figure out how to tune queries to run as quickly and efficiently as possible.

7. Compute

All companies need to budget for compute and optimize for its usage. These exercises will introduce you to different tools and their tradeoffs.

8. System Design – Case Studies

A list of case studies surrounding how real companies design infrastructure to support their business and the tradeoffs between the pieces they used and didn’t use. System Design questions like the ones at Uber.

9. System Design – Questions

Some questions to test out your system design skills! Modeled after real world questions. System Design questions like the ones at Lyft/Amazon/Amex. Connect the dots and see how these connect to AWS tools.

10. Product Improvement Via Data Engineering

At the end of the day, the goal of every employee is to add value to the business. 4 case studies on how data engineers contributed to the bottom line.

11. Algorithms Via Python

Learn about computer science fundamentals without having to overstudy, as many Data Engineers typically do. No CS background? No problem! Most data engineers don't come from a traditional Comp Sci Background. Python Data Engineer Scenarios just like the ones at Facebook. 

Premium Package

Includes everything in regular +

- 1-1 mentorship + unlimited access to author for life

- Solutions to course material/Personalized Feedback

- Premium Interactive Material - Hands On AWS/Data Modeling/System Design

- Resume Feedback/Negotiation Help

- Any guidance needed

Packages 

Regular: $36.99 – includes all the digital material

Premium: $14,000 (you may qualify for a discount if it is a good fit): please apply here: https://www.deinterview.com/apply

About The Author

Christopher Garzon has worked as a data engineer for Amazon, Lyft, and a Fintech Start up where he was responsible for building the entire Data Warehouse from scratch. He has helped hire data engineers at all of these companies and has trained 100+ candidates and helped dozens of students break into the DE industry.

 FAQ

Q: Can I buy this course with employee budget training? /

A: Yes, it is common practice for tech companies to pay for employee’s training and they normally set aside $3k-$6k. Discuss with your manager about getting this expensed. Let me know if you need my guidance/an invoice.

Q: Why did you create this course?

A: When I was in college I bought a similar mentorship program and it changed my life. The book was related to data science but unlike other courses that I found online, this course wasn't just focused on optimizing code and tuning ML models. Instead this course focused on product and impacting the business. This course helped shape my mindset not only for future career path, but my current job as well! I see a similar opportunity in the data engineer landscape where a lot of courses suggest studying 100 SQL and python problems, when in reality that is only a portion of the what gets asked of a data engineer role at companies like Amazon, Facebook and Google.

Q: Why is this course so cheap?

A: Relative to most bootcamps, this course is close to 50% cheaper because I have been able to work digitize the whole process and scale accordingly. Most bootcamps and/or mentorship programs do either one thing or the other - i.e it is either a virtual course and no 1-1 feedback or it is only ONE of the perks mentioned in premium (premium material OR 1-1 help etc). Competitors like InterviewKickstart charge $10k+ and offer a fraction of what is included.

Secondly, the return on investment is high. Studies have shown that you are more likely to complete a course when time and money have been invested. This is important because contrary to what is available online, it is becoming increasingly common for data engineers to make anywhere around $200k in their first year out of college. This quickly grows to ~$400k after a few years in the industry.

Q: What makes this course better than the free courses online?

A: Free courses rarely teach you what an actual business problem looks like that a DE might encounter on the job. This book focuses on how to best prepare you for the data engineer world without having to become an expert at every single aspect. You can do an Amazon Web Service (AWS) course that might take you a few years to master. This book, however, focuses on accelerating your path to becoming a DE (or enhancing your skills at your current role), so you can learn everything else on the job. 

Q: I am interested in Data Science, do you provide mentorship surrounding this?

A: Currently I do not, but my mentor does and I highly recommend his course.

This is the same mentor that helped me break into the tech industry at Amazon right out of college. Similarly, discuss with your manager about expensing this purchase at your current work place.

Q: I have a different question, where can I ask it?

A: Please feel free to email me at chrisgarzon19@gmail.com.