Re:learn Data Structure & Algorithm as a Data Engineer

• 2 min read

AI helped me to re:invest Data Structure Algorithm Non-Traditional Journey with DSA

I didn’t start with a computer science degree. I didn’t have a roadmap handed to me. What I did have was a stubborn belief that effort trumps excuses. So I took a leap into tech, knowing I’d have to claw my way through learning DSA (Data Structures & Algorithms) on my own.

Let’s be real: I’m no “DSA guru” (yet!). But what I’ve been doing:

  • Connecting algorithms to real-world data engineering challenges (think optimizing pipelines, wrangling messy datasets, or scaling systems).
  • Learning by doing—not just memorizing theory.
  • Owning the fact that growth is messy, iterative, and totally worth it.

Now? It’s time to revise, rebuild, and level up my DSA foundation. Why? Because in tech, standing still means falling behind.

Btw, I am not a big fan of DSA ping-pong discussion in the interview whereby most of the company uses Technical Interview but it would be a bit different depending on the size, culture, business of company.

I would recommend you and myself to experience it even I was failing in the interview sometimes with the DSA questions by interviewer and how to explain the solutions to them. Nowadays, I use the Gen AI to resolve almost the technical questions that helps me to have more time to focus on the System, platform and integration parts.

But I strongly agree that we will go far with DSA.

→ Details are available at Data Structure & Algorithm Patterns Post and DSA for Data Engineering Section in Handbook.