Webinar: Unlock Data Engineering Excellence
Explore the Data Engineer Apprenticeship (Level 5)

Webinar details
Looking to launch or accelerate your career in data engineering?
Join our Data Engineer Apprenticeship Webinar to discover how you can gain practical, hands-on experience in building and maintaining data pipelines, modelling data, and unlocking the full value of business data. Learn how to support data-driven transformation within your organisation while developing the core technical and leadership skills employers are looking for.
Why Attend?
Whether you're starting out in tech or looking to boost your existing skill set, the Data Engineer Apprenticeship gives you the tools to thrive in today’s data-driven world. You'll learn how to design and manage robust data pipelines, model and transform data to unlock insights, and help your organisation make smarter, faster decisions. This programme offers hands-on experience with real-world data challenges - empowering you to drive data-led transformation, support AI initiatives, and become a key player in your organisation’s digital evolution.
Date & Time:
📅 Date: 24 June 2025
⏰ Time: 12 pm to 12.30 pm [BST]
Agenda:
Attend this webinar if you are:
- An aspiring data engineer seeking to enter the field.
- A Professional aiming to upskill and transition into a data engineer role.
- A HR or Learning & Development professional seeking to upskill your workforce and empower them with data engineering expertise.
About Apprenticeships
Apprenticeships provide a well-defined route for internal talent development in England*. Apprenticeships are available to both new and existing employees who work 30 hours or more per week. Funded by the Apprenticeship Levy, apprenticeships are a cost-effective way to upskill employees in analytics, data science and AI.
*If not employed in England, apprenticeships can be funded commercially.

Elle Neal, Data Scientist at BPA Quality
“Doing the apprenticeship has helped me glue everything together and fill the gaps in the knowledge I had to make me more confident in delivering the data science models I want to create.”