Data Engineer

Python is the first language for writing pipelines and automation — most tools in the field are written in or wrap it.

SQL is the backbone of data engineering — you need it at every step from extraction to transformation to quality checks.

Pipelines live on Linux servers — comfort with the terminal and Git is required for any professional, collaborative work.

A bad model makes queries slow, costly, and wrong — good modeling is the foundation of an efficient warehouse.

The core of data engineering is moving data from source to destination cleanly, reliably, and repeatably — that's what pipelines do.

When pipelines multiply and their dependencies tangle, you need a tool to manage scheduling, retries, and monitoring instead of fragile cron scripts.

Modern infrastructure runs on the cloud — you must separate storage from compute and control cost and performance.

Not all data is structured — a data lake absorbs raw data, and the lakehouse combines its flexibility with warehouse reliability.

When data grows to terabytes, single-machine tools collapse — Spark processes data distributed across whole clusters.

Much of today's data needs real-time, not batch, processing — streaming moves your data the moment it happens.

A pipeline that runs but passes bad data is more dangerous than a broken one — quality is what preserves trust in everything above it.

Production pipelines need the same discipline as code: automated deployment, containers, and monitoring — to stay reliable and reproducible.

As data grows, so does your legal and ethical responsibility — governance protects the organisation from leaks and violations.

Engineered data serves different consumers — analysts, dashboards, and ML models — each with distinct needs and shapes.