Builds the pipelines every AI system depends on. Chronically in demand and often overlooked.
A data engineer builds the pipelines that move, transform and store the data that every AI system depends on. Without data engineers, data scientists have no data to analyse and ML engineers have no data to train on. The role is chronically in demand and often overlooked by people entering the AI field.
Data engineering is less glamorous than model building but arguably more important. Bad data causes more AI failures than bad algorithms. A well-built data pipeline is the difference between a model that works in production and one that fails unpredictably.
Write pipelines, maintain existing infrastructure, fix data quality issues. Learn SQL deeply and understand the data landscape.
Design data models and pipeline architectures. Own a domain's data infrastructure. Optimise for cost and performance.
Architect the data platform. Define data governance, quality standards and modelling conventions. Lead complex migration projects.
Set the data strategy for the organisation. Influence technology choices, team structure and data culture.
Already working in another field? Here is how your background maps.
You already know databases and APIs. Learn data warehousing, pipeline orchestration (Airflow) and SQL at an advanced level. Your software engineering rigour is an asset — many data engineers write sloppy code.
You know databases deeply. Learn cloud data warehouses, pipeline tools and Python. Your understanding of query optimisation and schema design transfers directly.
Focus on SQL (learn it very well), Python, and one cloud platform. Build a project that ingests data from an API, transforms it, and loads it into a warehouse. This demonstrates the core skill loop.
