Technical skills can be taught.
But discipline and patience?
That has to be earned.
It comes from failures.
From experience.
From realizing that no one is coming to save you.
#latenightthoughts#discipline
Learn SQL.
Learn Python.
Learn PySpark.
You’ll become a Data Engineer. ❌
Understand why, when, and how to use these tools to solve real data problems.
That’s what makes you a Data Engineer. ✅
Most people think Data Engineering is all about tools!
But in reality, it’s about solving problems.
The requirements decide the architecture, and the architecture decides the tools.
Don’t start with: “Which tool should I learn?”
Start with: “What problem am I solving?”
Our only unfair advantage left is wondering.
Not knowing. Not having the answer. Just being curious enough to ask, "What if?"
Machines are getting better at knowing. They are getting better at finding answers, connecting ideas, and even producing things we once needed years to learn.
Perhaps our advantage is not in knowing more, but in knowing what is worth wondering about.
Just checked: in the last 6 months, they have tried a few AI products (possibly the reason).
When most candidates prepare for skill ‘X’, it’s difficult to convince a company to hire for skill ‘Y’. The company that does it will lose out on the talent.
As you stated in one of your tweets, you did DSA just to get into any company. So as a company, if I ask you to do something different, you won’t put in the time to learn and appear (unless it’s a Google or a big fat salary is offered).
Anything that involves just AI and not fundamentals, in my opinion, will have higher false positives, making hiring extremely difficult.
Current AI Job landscape
- AI solution architect
- AI/ML engineer
- AI workflow architect
- Prompt engineer
- AI/ML Ops engineer
- Ethical AI specialist
- AI data governance specialist
- Convesation AI designer
- Sentiment Intelligence analyst
- AI Operations analyst