The pipelines AI systems depend on.
Data Engineering & AI Data Platforms
Every AI system is a data system first. You build batch and streaming pipelines, model warehouse layers, handle quality and lineage, and prepare data for retrieval and embedding workloads.
What you learn
Python, SQL and how data moves through systems.
Ingestion, transformation and warehouse modelling.
Orchestration, quality checks and embedding pipelines.
Reliability, cost awareness and incident handling.
An end-to-end pipeline you can explain under questioning.
Protected programme depth
Illustrative redaction. Detailed module content, lab briefs and assessments are shared with enrolled candidates.
Technologies
Roles this prepares you for
- Data Engineer
- Analytics Engineer
- AI Data Platform Engineer
What you leave with
- A pipeline that runs on a schedule and fails loudly
- Modelled data another engineer could query without a briefing
- An embedding workflow feeding a retrieval use case
Duration & fees
Confirmed during consultation, based on your entry point and cohort. Published figures will appear here once finalised.
Admissions & fees →Questions about this programme
It can be, if you are prepared to work through the programming foundation properly. Data engineering is a software role, not a reporting role.
It covers the data side that AI systems depend on — retrieval sources, embeddings and pipeline reliability — rather than model serving itself.
One conversation
changes the route.
Tell us where you are. We'll tell you honestly what the path looks like from here.