The data engineering market is saturated with 100+ applications per job posting.
The number of tools/experiences listed in job descriptions is insane.
The gatekeeping is real!
It’s no wonder people struggle to break into a high-paying data engineering job.
How to go from simple SQL to be able to handle challenging use cases?
You consider yourself “okay at SQL”, but unsure how to get to “good/advanced” levels?
You aren’t sure what stakeholders/interviewers are even talking about or asking for.
Employers are looking for more than just coding ability.
You need to handle complex queries, perform performance tuning, and follow best practices.
Projects/resources from YouTube and blogs don’t explain real-life data problems.
“These tools and frameworks can only be learnt on the job” - common advice
But how would you get a job if you can’t get the experience ? It’s a chicken-and-egg problem.
In interviews, you’ll be asked about performance and data quality.
Without real experience, how can you confidently answer these?
You know you can put in the work to land a well-paying data job. But you don’t have a step-by-step guide to get you there.
What if employers are excited to hire you?
Employers are looking for someone they can trust.
Build trust quickly with your knowledge of data engineering concepts and their trade-offs.
Being able to make architectural decisions based on the appropriate trade-offs will set you apart.
E.g., Anyone can write code to partition a table, but why is partitioning necessary in the first place?
Be the problem solver every employer is looking for
Solve business problems with technology.
Demonstrate expertise by focusing on business outcomes.
Enable data-driven decision making for any org.
Make your stakeholders’ lives easy, and you will go far in your career.
Waiting for a high-paying data engineering job to somehow land on your lap is a losing strategy. Here is what you need to do to get there.
"As a budding data engineer I found this a brilliant resource that ties the important concepts together in a way that brings real clarity. This is superior to other free and paid resources I've used in the past, I actually feel equipped now to tackle projects."
— Student
"I would highly recommend this to my colleagues in the Data team and juniors who are still in college or just joined a company. I believe being a good data engineer is less about mastering tools but about understanding Whats/Hows/Whys of data. This course is helping me be a better engineer :)"
— Student
Learn concepts that get data engineers hired
Be in demand by delivering outcomes using data engineering concepts implemented with industry-standard tools.
Here are the key data engineering concepts that you will learn in this course
Data Warehousing: Build tables analysts actually want to usePipeline Design: Handle late events, backfills, and failures gracefullyData Flow(Medallion): Standardize how data flows through your systemScheduling and Orchestration patterns: Create pipelines that produce output data on timeData Storage Patterns: Choose the right storage strategy to make analytics fast and cost-effectiveDistributed Data Processing Patterns: Scale your pipeline confidently as data volume grows
You will learn the common problems the data team faces.
And how the concepts above help you deal with them.
Tools you’ll use:
Deliver the right data on time
Learn how to get high-quality data to stakeholders on time, in an easy-to-use format.
Every tool/framework/concept is a means to do this.
In this course, you’ll learn key DE concepts, when & how to use them, and their trade-offs.
Don’t spend months or years trying to piece together random, outdated online tutorials.
Follow a step-by-step path to learn the essential concepts of data engineering starting now.
Build trust with outcome-focused capstone projects
You’ll learn how to go from stakeholder needs to a data product that serves those needs.
Build trust by focusing on stakeholder needs
You’ll learn how to demonstrate expertise with a GitHub README, focusing on outcomes.
Here are the capstone projects you will build
- Data Warehouse for advertisement analytics.
- Data Warehouse built with 50GB+ real StackOverflow data
You’ll learn how to show outcomes based on real data (E.g., StackOverflow User Trends)
Learn how to build end-to-end project by following a step-by-step approach. Impress potential employers with your outcome focussed projects.
Enroll in my Data Engineering Course and start learning the key data engineering concepts and how to apply them in real life.
Demonstrate your value by enabling data-driven decision making.
"This workshop covered many important topics such as how to organize code, how to write modular code, and what a standard industry-level data engineering project looks like. These are aspects that most people on YouTube tend to neglect, but you addressed them in depth. This content has been very helpful for me & gave me the clarity I was looking for."
— Student
"I knew the tools, but never knew how to put them all together. Your posts are well-detailed, but the workshop demystified things for me. Because your code-organization is consistent, I think I'll now be able to run through various tools/examples."
— Student
Complete bundle
$900 → $499 early bird
Self-paced video course · One-time payment · Lifetime access
Total content
~10 hrs
Examples & exercises
160+
Office hours
Tue & Thu
7–9 PM EST
Weekly office hours
Live with Joseph Machado
Tue & Thu · 7:00 PM – 9:00 PM EST
May 1 – July 4, 2026 · 2 months
What you'll learn
- Data Warehouse design & Medallion Architecture
- Pipeline Design Patterns & Data Quality checks
- Orchestration & Scheduling Patterns
- Spark API & Data Storage Optimization
- Data Processing & Code Optimizations
- Interview Prep & two capstone projects
Sample Chapter
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