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Applied Data Engineering

Emboldening data engineers to create powerful, scalable solutions with end users in mind.

Overview

The Applied Data Engineering Programme is designed to transform data capabilities and drive impactful, scalable business solutions. Apprentices will master a wide range of next-generation technical skills while developing a product-focused mindset, enabling them to critically evaluate workflows and stakeholder needs. Throughout the programme, they will become proficient across a spectrum of engineering operations, ensuring the implementation of high-quality workflows and best practices in critical areas such as data modelling, storage, and change management.

Duration

17 months

Price

Free tuition

Level
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5

Most qualifications have a difficulty level. There are 9 qualification levels. The higher the level, the more difficult the qualification is.

Qualification

Apprenticeship

Who it's for

Data professionals skilled in Python and SQL, who are looking to progress into a data engineering role.

To apply you'll need

  • To have the right to work in the UK
  • To have lived in the UK or EEA continuously for the past 3 years
  • To have at least a grade of 4/C GCSE (or equivalent) in Maths and Englis
  • To have not previously studied the course content
  • To not undertake any other qualifications during the apprenticeship
  • To be able to apply your learning to your role

Qualifications Received

  • Level 5 Data Engineer apprenticeship standard

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Modules

Module one

Data systems and architecture

Apprentices gain an understanding of different data infrastructures and analyse their organisation’s architecture, systems, and processes to explore diverse use cases throughout the data management lifecycle.


Module two

Data modelling

This module explores data model designs according to business needs. With a grasp of the technical structure of different data systems, apprentices delve into data warehousing, data meshes, and data lakes.


Module three

Data pipelines

Apprentices gain the knowledge and tools to build and manage ETL pipelines, exploring how to extract, transfer, integrate, and ingest data. In the process, they assess and incorporate data quality frameworks, define quality metrics, document needs, and establish oversight procedures.


Module four

Automating data pipelines

With tools like Kafka and Airflow, apprentices develop the skills needed to automate ETL data pipelines. This module also covers how to incorporate security, scalability, and governance into pipeline design.


Module five

Testing data pipelines

The final module of phase one focuses on testing, monitoring, and evaluating data pipelines. Topics covered include pipeline design with a focus on observability, pipeline performance monitoring, and optimising data processes for reliability.


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