Applied Data Engineering Programme Overview
This programme helps data professionals master next-gen technical skills while developing a product-focused mindset. Learners gain skills to implement solutions across the spectrum of engineering operations, ensuring best practice in data modelling, storage, and change management. It is part of our Data Academy and AI Academy Programmes.
Key Skills Gained:
Data systems and architecture
Data modelling
Data solutions design
Data governance
Data change management
Machine learning
Business Outcomes:
Build an innovative data function: Empower teams with strong commercial awareness and a product mindset, helping them design data pipelines and architecture that aligns to business needs.
Increase efficiency and drive AI implementation: Automate manual data processes and create structured datasets to power AI workflows.
Increase data capability and capacity: Build scalable, adaptable solutions that can handle growing demand for data and fast-moving business needs.
Apprenticeship Qualification Achieved: Level 5 Data Engineer
Duration: 14 month delivery, plus 3 month assessment
Applied Data Engineering Indicative Curriculum Breakdown
Phase 1: Building your data engineering toolkit
Module 1: Data systems and architecture
Data infrastructure
Data acquisition and enrichment
Continuous learning for data engineers
Module 2: Data modelling
Schema design and data mapping
Data warehousing, data lakes, and data meshes
Module 3: Data pipelines
Ensuring data quality
Extracting and transferring data
Data integration and ingestion
Module 4: Automating data pipelines
Data pipeline automation
Pipeline deployment
Module 5: Testing data pipelines
Pipeline troubleshooting
Pipeline testing and observability
Phase 2: Product-focused development
Module 6: Data security, governance, and ethics
Module 7: End point assessment (EPA) readiness
Module 8: Data solutions for business impact
Module 9: Transformative development methodologies
Module 10: Data change management
Phase 3: Scale and innovation for enterprise
Module 11: Real-time data processing
Module 12: Cloud engineering
Module 13: Critical incident response
Module 14: Data engineering for machine learning
Gateway and independent preparation for end point assessment (EPA)
Months 15-17
Note: This is an example curriculum, and specific details may vary per cohort.
Applied Data Engineering Indicative Delivery Model
Monthly delivery model, approx. 26 hours per month total commitment. The exact time commitment will be outlined in the training plan that apprentices will receive at the start of their apprenticeship.
Structured Learning (~40% - 10 hours/month):
Asynchronous learning (3.5 hours): Online, self-paced content that sets the foundation of skills for the module.
Live workshops (3.5 hours): Instructor-led, interactive learning that dives deeper and reinforces the asynchronous content.
Group coaching (1 hour): Peer support and feedback on project deliverables.
Coach support (2 hours): Includes tutoring, progress reviews, & other individual/group support.
Working in Existing Role (~60% - 16 hours/month):
Project and applied learning (16 hours): Structured and unstructured application of learning to apprentices’ roles.