Started in data engineering because information trapped in silos drives me insane. Well, not really. Came for the love of tech. Stayed because fusing tech and automation with data solves problems and that makes people happy!
Watched companies spend millions on "digital transformation" that fails because nobody wants to deal with the actual hard part: making the data usable.
Built pipelines for financial data, customer records, logistics systems, trading info etc. Same problem everywhere, good data buried in bad structure.
AI (or specifically language model) changes the interface. The engineering challenge remains the same. Connect sources. Clean mess. Make it reliable.
This is not a pivot to AI. It's applying 10+ years of data engineering to the newest interface for accessing information.
The AI or LMs are a commodity. The data engineering is the differentiator.
One person for now. Intentional. Better to do 5-7 implementations excellently than 50 adequately.
If your data challenge requires a team, transparent about that upfront. If it's solvable solo, pricing reflects that efficiency.
