I didn't wake up one day and decide to become a data engineer.
Like many people from a non-computer science background, I started with curiosity and a lot of confusion.
I'm Mahamudur (also known as Raja / Bhairus). I come from an engineering background that had nothing to do with data at first. No fancy buzzwords. No "AI since childhood" story. Just a simple question that slowly grew louder over time:
"How do companies actually use data to make decisions?"
That question changed everything.
What Is Data Engineering? (Explained Like a Story)
Before talking about tools, let me explain data engineering the way I understood it when I was a beginner.
Imagine a company as a living body.
- Data is the blood
- Analysts and data scientists are the brain
- Dashboards are the eyes
- And data engineers?
We are the heart and veins, quietly making sure data flows correctly, reliably, and on time.
A data engineer builds systems that:
- Collect data from different sources
- Clean and transform that data
- Store it efficiently
- Make it ready for analysis, reporting, and AI
If data is broken, delayed, or unreliable everything else fails.
My First Exposure to Data (And Why It Felt Overwhelming)
When I first heard terms like:
- SQL
- ETL
- Pipelines
- Warehouses
- Big Data
…I felt lost.
I was working professionally, learning after office hours, and slowly understanding that data engineering is not about one tool. It's about thinking in systems.
That mindset shift was the hardest part.
The Core Basics of Data Engineering (No Fluff)
Here's how I now explain data engineering to absolute beginners.
1. Data Sources
Data comes from everywhere:
- Applications
- Databases
- APIs
- Logs
- Sensors
- Business systems
As a data engineer, you don't control how data is created you deal with the reality of it.
2. Data Ingestion
This is how data enters the system.
It can be:
- Batch (daily, hourly)
- Streaming (real-time)
Early in my career, I learned that reliability matters more than speed.
3. Data Transformation (The Real Work)
This is where data engineering becomes engineering.
- Cleaning bad data
- Handling missing values
- Joining multiple systems
- Applying business logic
This is where SQL becomes a superpower and why I strongly believe every data engineer must master it deeply.
4. Data Storage
You don't store data randomly.
You choose:
- Data warehouses
- Data lakes
- Lakehouses
Based on:
- Cost
- Performance
- Use cases
Poor modeling here can cost companies millions.
5. Serving Data
At the end of the pipeline:
- Analysts query it
- Dashboards visualize it
- ML models consume it
- Business leaders rely on it
If this layer fails, trust in data dies.
Why I Chose Data Engineering as a Career
I currently work as a Data Engineer at TCS, and my journey took me all the way to Budapest, Hungary, working onsite in a global environment.
What attracted me to data engineering wasn't hype it was impact.
- You work behind the scenes
- Your code touches every department
- Your decisions affect performance, cost, and trust
It's not glamorous, but it's powerful.
Learning Data Engineering the Hard Way (And the Right Way)
I learned that:
- Tools change
- Fundamentals don't
That's why I focus heavily on:
- Advanced SQL
- Data modeling
- System design
- Performance optimization
- Cloud-native thinking
And I share this learning publicly, through:
- My website (bhairus.com)
- My YouTube channel (Bhairus)
- Blogs like this one
Not to teach from a pedestal, but to learn in public.