This is the exact framework I use to keep my team motivated, track growth, and make sure everyone knows what it takes to move from one level to the next. No guesswork involved.

If you work with data and live in the terminal, these CLI tools belong in your daily toolkit. Lightweight, fast, and built for real productivity.

Raw data alone doesn’t drive action—stories do. Learn how to use the “What, So What, Now What” framework to turn complex data into compelling narratives that influence decisions.

Going beyond senior isn’t just about writing better code. It’s about impact, strategy, and navigating complex systems. Here’s how to take the next step in your data engineering career.

Say goodbye to sluggish dependency management. uv is a modern, high-performance package manager designed for Python power users. Here’s everything you need to know to get started.

Europe’s data infrastructure is built on US clouds. But what if that changes? With rising tensions and no European alternative, data engineers must start thinking about Plan B—before it’s too late.

Most companies underfund data engineering—until it's too late. Here are three key reasons why it happens and how you can turn things around.

From setting up Apache Airflow to writing dynamic DAGs, this hands-on tutorial covers everything you need to know to master workflow automation and take control of your data pipelines.

5 red flags you may need to work on. I’ve stopped interviews for the 10th minute because of #4.

Behind the scenes of PySpark: decode its architecture, learn how it handles transformations and actions, and optimise your workflows for high-speed data engineering.