๐๐ก๐ฒ ๐'๐ฆ ๐๐๐ญ๐ญ๐ข๐ง๐ ๐จ๐ง ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ ๐๐ฎ๐ซ๐ข๐ง๐ ๐ญ๐ก๐ ๐๐ ๐๐จ๐จ๐ฆ.
Everyoneโs betting on AI. Iโm betting on the foundation beneath it.
Everyoneโs talking about AI.
But hereโs the part no one talks about:
AI doesnโt work without clean, organized, accessible data.
Before you can train models or deploy machine learning tools, someone has to build the pipelines and systems that make raw data usable.
Most companies skip this step, and they pay for it later.
Data engineering is the process of collecting raw data, cleaning it, making it usable for analytics, machine learning, and other operations.
Right now, demand for those skills far exceeds supply.
And most business leaders donโt even realize it.
They assume hiring a data scientist or analyst covers it all.
It doesnโt.
Over the past month Iโve interviewed at a few mid to small size businesses (non tech industry), and all of them wanted to incorporate AI into their organization, and they wanted me to do it as the sole data analyst without any existing big data infrastructure.
Face palm.
Iโve observed a huge gap, when it comes to understanding what it takes to get to the point where AI can be built in-house.
Most canโt even get their data into one place.
Organizations arenโt ready for AI. Not even close.
Itโs the .01% making all the noise in the headlines.
Everyone else is 10โ20 years behind.
If you want to leverage the latest tech, get the basics right first:
Start by hiring a data engineer.
Thatโs why Iโm doubling down on learning how to build the infrastructure that AI runs on.
3 Resources Iโm Using To Up-Skill For The AI Boom:
If you're looking to get started in data engineering, these are the 3 resources I recommend:
Data Engineering Certification by (Joe Reis ) w/ DeepLearning.AI
A great high-level overview of the field. The guided labs with AWS provide an opportunity for hands on learning with real world infrastructure.
The Data Warehouse Toolkit by Kimball and Moss
A complete guide to data warehousing best practices - arguably the most important pillar for data engineers to really nail.
Fundamentals of Data Engineering by Joe Reis and Matthew Housley
Covers modern tools and workflows, and how they fit into todayโs data engineering lifecycle.
From Biotech to Data Analytics.
From Data Analytics to Data Engineering.
There has been one constant in my young career;
an affinity for pivots.
A desire to learn new skills at a fast rate.
Iโm not here to fit in a box. Iโm building my own.
Iโm leveraging jobs to stack skills that I want to learn.
By stacking enough of these skills together, I unlock the ability to recognize greater opportunities.
This is how Iโm building leverage.
I feel blessed to have you along for the journey.
If you havenโt already, subscribe.
And most importantly,
keep asking for more.
-Warren