Data Science · Live cohort

Data Engineering

Build the foundations behind reliable analytical data.

Develop the practical thinking behind moving, transforming and organising data so analysts, products and reporting systems can depend on it.

Next cohort

Next cohort details will be published when confirmed.

Register interest
The programme standard

Understand it. Practise it. Produce evidence.

ClarityKnow the concepts and reasoning behind the work.
PracticeUse tools and methods in realistic tasks.
EvidenceLeave with practical outputs you can inspect and explain.
How the work fits together

The data flow

Reliable analysis begins before the analyst opens a dashboard.

Engineering work earns trust when the flow can be monitored, explained and repaired.
01Source

Understand where the data originates and what can go wrong.

02Transform

Clean and organise data into dependable structures.

03Orchestrate

Move from one-off scripts to repeatable workflows.

04Observe

Make quality, failure and dependencies visible.

Programme overview

Data Engineering

Develop the practical thinking behind moving, transforming and organising data so analysts, products and reporting systems can depend on it.

01
Understand data movementFollow the path from source systems into usable analytical stores.
02
Design repeatable pipelinesStructure extraction, transformation and loading as controlled workflows.
03
Work with data qualityBuild checks that make failures and inconsistencies visible.
04
Document the systemExplain sources, transformations, dependencies and operational expectations.

What you will build

Practical work that makes progress visible.

01 · Output
Pipeline design brief

A clear description of sources, transformations and destinations.

02 · Output
SQL transformation project

A structured set of transformations for analytical use.

03 · Output
Data quality checks

Simple rules and tests that expose unreliable inputs.

04 · Output
Workflow documentation

A readable operating guide for the pipeline.

05 · Output
Monitoring checklist

What to watch, what can fail and how to respond.

06 · Output
Capstone data flow

A complete small-scale engineering workflow from source to analytics.

Programme journey

A clear progression through the work.

Foundation

Data systems and architecture

Understand where engineering sits between sources, storage and analytics.

Core

SQL and transformation

Build dependable transformations and model analytical data.

Pipeline

Orchestration and repeatability

Move from one-off scripts to scheduled, observable workflows.

Quality

Validation and monitoring

Make data quality and failure states visible.

Delivery

Documentation and capstone

Bring the workflow together into a system another person can understand.

Who this is for

Built for people who need the capability in practice.

Analysts moving towards engineering
Developers working with data systems
Technical graduates
Teams that need more reliable data pipelines

How you learn

A live learning rhythm built around practice.

The programme combines structured teaching, guided practice and regular review so the work develops alongside the knowledge.

01Live instructor-led sessions
02Guided technical practice
03Reusable templates and examples
04Capstone workflow
05Recordings and clinic support

Next step

Interested in this programme?

Check the current application status and register your interest. We do not publish cohort dates until they are confirmed.

Next cohort

Next cohort details will be published when confirmed.

Register interest