Skip to main content

Applied Data Engineering

Multiverse Applied Data Engineering: scalable data solutions and data capability, aligned to Level 5 Data Engineer apprenticeship.

Updated over a week ago

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.


Did this answer your question?