Data Engineer Level 5 Apprenticeship

Data Engineer Level 5 Apprenticeship

Build, manage and optimise data systems that power business intelligence and AI
Master advanced data engineering to build systems that collect, manage and convert data into usable information. This apprenticeship teaches data pipelines, ETL processes, cloud platforms, data warehousing and DevOps practices – creating scalable infrastructure for data-driven organisations.

The Data Engineer Level 5 Apprenticeship is a government-funded programme β€” covered by the Growth and Skills Levy for most employers β€” that develops data engineers who build the systems underpinning business intelligence, analytics, and AI. Learners master Python, SQL, cloud platforms, data pipelines, ETL processes, and infrastructure as code. As AI adoption grows, data engineers are increasingly responsible for building the infrastructure that LLMs and AI applications depend on β€” pipelines, vector databases, data quality frameworks, and scalable cloud architecture. This programme prepares engineers for that reality, not just traditional data warehousing.

For employers: Build scalable, reliable data infrastructure. Ensure continuous access to critical data for informed decisions. Improve data governance, reduce technical debt, and create the foundation your AI initiatives need to function. The Coders Guild led the R&D that created the UK’s first Level 4 AI and Automation apprenticeship standard with Skills England and the Alan Turing Institute β€” AI-readiness is part of how we think about data engineering, not a separate conversation.

For learners: Master Python for data engineering and automation. Build SQL and NoSQL database skills. Design and implement ETL pipelines and data warehouses. Work with cloud platforms (AWS, Azure, GCP). Apply DevOps practices including CI/CD, Docker, and infrastructure as code. Understand how to build data infrastructure that supports AI and machine learning applications. Develop leadership and stakeholder management capabilities.

Application β†’ Training β†’ Portfolio Building β†’ EPA β†’ Qualified Data Engineer

Course content

Course content

Programming & Development

  • Advanced Python for data engineering
  • Data processing with pandas and libraries
  • Version control with Git
  • Software testing and quality assurance

Database Engineering

  • SQL mastery – from basics to advanced queries
  • NoSQL databases and when to use them
  • Database design, normalisation and optimisation
  • Data warehousing (star schemas, data lakes, data marts)

Data Pipelines & ETL

  • Data engineering lifecycle and data modelling
  • Building and automating data pipelines
  • ETL/ELT processes and tools
  • Data integration across systems
  • Streaming and real-time data processing
  • Vector databases and data infrastructure for AI and LLM applications

Cloud & Infrastructure

  • Cloud platforms (AWS, Azure, GCP)
  • Infrastructure as code with Terraform
  • Containerisation with Docker
  • CI/CD pipelines with GitHub Actions
  • Kubernetes and orchestration

Data Governance & Quality

  • Data quality frameworks and monitoring
  • Data security and compliance
  • Metadata management and lineage
  • Performance optimisation and troubleshooting

Product & Leadership

  • Product management for data products
  • Stakeholder management and communication
  • Building sustainable data products
  • Presenting to technical and non-technical audiences

Delivery

Delivery

Online delivery with monthly group sessions, 1:1 coaching, and webinars. Practical, challenge-based learning using real data infrastructure projects. Learners build a portfolio throughout, with progress reviews tracking evidence against the Level 5 standard.

Assessment

Assessment

End-point assessment (EPA) including portfolio review and professional discussion. Apprentices build evidence throughout using real engineering projects – data pipelines, infrastructure code, database designs, automation scripts, cloud deployments – demonstrating scalable solutions and business impact.

Want to Become an Apprentice?

Want to Become an Apprentice?
Apply online through our simple formJoin our talent poolWe match you with real employersStart earning while you learn

FAQs for Employers

FAQs for Employers

How does the funding work?
The Data Engineer Level 5 Apprenticeship is funded through the Growth and Skills Levy. If your organisation pays the levy (wage bill over Β£3 million), training comes directly from your levy pot at no additional cost. If you’re an SME with a wage bill under Β£3 million, the government covers 95% of training costs β€” you contribute 5%. We handle all the compliance and admin and guide you through the process from start to finish.
Yes. Every apprenticeship is built around the apprentice’s real job. We collaborate with you to shape project work and tasks that directly support your team’s priorities, goals, and systems.
We’re platform-agnostic and industry-led. We teach core engineering principles that work everywhere, with elective pathways for AWS, Azure or GCP. We adapt to your infrastructure and tech stack.
From onboarding to endpoint assessment, you’ll have access to our support team, technical coaches, and account managers. We handle compliance and reporting, keep you in the loop on progress, and are always here if anything’s not working.
Definitely. Apprenticeships are ideal for upskilling or reskilling current employees. It’s a practical way to retain talent, deepen expertise, and build internal capability in a cost-effective way.
You’ll see the impact in-role. Apprentices bring their learning straight into the day job. We also check in with managers, gather regular feedback, and provide reporting so you can see development over time.
Data analysts moving into engineering, software developers transitioning to data roles, junior data engineers needing formal training, or database administrators expanding into cloud and pipelines.
We teach DevOps practices as part of the apprenticeship. Understanding CI/CD, Docker, and infrastructure as code are built in – they don’t need prior DevOps experience, just solid programming foundations.
Yes β€” and this is increasingly the primary reason organisations come to this programme. AI applications don’t work without clean, accessible, well-structured data. Your data engineer builds the pipelines that feed AI systems, the data quality frameworks that keep them reliable, and the vector databases and cloud infrastructure that LLMs need to function at scale. Without that foundation, AI initiatives stall or produce unreliable outputs. An apprentice who understands both traditional data engineering and modern AI infrastructure is genuinely difficult to hire β€” this programme builds exactly that capability.

FAQs for Learners

FAQs for Learners

Do I need to be very technical already?
You need solid programming foundations and database experience. This is Level 5 – designed for people with some technical background who want to specialise in data infrastructure and engineering.
Yes. Our training adapts to your workplace. We focus on engineering principles that work everywhere, with elective pathways for your specific cloud platform (AWS, Azure, or GCP).
You’ll spend 6 hours a week on off-the-job learning – things like workshops, mentoring, group projects, and self-study. It all fits around your work schedule, and we help you manage it in a sustainable way.
You’ll have access to tutors, technical coaches, and peer groups throughout the apprenticeship. We’re here to answer questions, troubleshoot challenges, and celebrate your wins. Plus, you’ll check in regularly with your line manager.
Nope. This is workplace-first, challenge-based learning. You won’t be stuck in lectures. You’ll learn by doing, solving real problems, working with peers, and applying everything in context.
You’ll complete an end-point assessment and, if you pass, gain a nationally recognised qualification. Most apprentices continue in their role, take on more responsibility, or move up into a more advanced position.
Yes. This is a full-time employed role with a salary. You earn while you learn and build real data infrastructure from day one.
12-18 months typically, depending on your starting point and experience. Those with strong programming backgrounds may complete faster.
Data analysts wanting to move into engineering, software developers transitioning to data, database administrators, or anyone with programming skills who wants to specialise in data infrastructure.

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