Data Engineering's Durable Skills: What AI Can't Automate
The data engineer who survives the next five years isn't the one who codes the fastest — it's the one who understands context. Here's what that means in practice.
The data engineer who survives the next five years isn't the one who codes the fastest — it's the one who understands context. Here's what that means in practice.
For years I shipped Apache Beam pipelines as one giant file in a Dataflow Flex Template because custom containers never worked for me. The fix I had missed: a Flex Template is two images, a launcher and a worker, each with its own entrypoint.
The first agent I worked on had evals, but they only ran in CI/CD: a pre-flight checklist with no in-flight instruments. Here is the case for live, continuous evals as part of your observability: what to measure, what a decent score looks like, and how to tune them over time.
The right AI tool depends on who's asking, and every tool only works if it's grounded. How I split agentic tooling by persona for a fictional bank: DaaS golden paths for customers, a knowledge-graph-grounded analytics assistant for analysts, and a shared RAG knowledge base.
A walkthrough of the RAG-powered chat widget on this site - how it indexes blog posts, how grounding actually works, and why you don't need expensive managed services to build something solid.
You know you've created value when you've built a product that earns people's trust. A story from a loss-prevention project at AutoZone, and a manufacturing COO who ran the same playbook on a shop floor.
At Google Cloud Next '26, a conversation about genBI made me rethink nearly 20 years of BI assumptions. Here are three shifts worth considering.
I've been interviewing QA/QE candidates lately, and the bar is not what I expected to find. Here's what I'm actually evaluating - shared in the spirit of helping the right people level up.