Foundations and Modern Practices of Data and AI Engineering
Clean, well-structured, easy-to-access data can be of enormous benefit to you and your organisation. Setting up automated pipelines to inject such data into your company can improve machine learning models, inform analytics, and give context to AI agents that your team will be able to rely on every day.
Everyone wants to make data-driven decisions, and with the rise of AI agents — tools that plan, reason, and take action on their own — the pressure for quality data is really on. Here’s the reality: an AI agent is only as good as the context you give it. If your raw data is messy or scattered, your AI will stumble. Getting that data collected, cleaned, and structured so you and your machines can actually use it is the real work — and that’s why data engineering has become such a make-or-break discipline.
The Foundations and Modern Practices of Data and AI Engineering program is designed for those who already have a good understanding of coding and data wrangling. It will give you real-world, up-to-date skills to build reliable, scalable pipelines that help make your organisation's data truly AI-ready.
Watch the video and listen to the instructor's views.
Benefits
In today’s data-driven world, businesses that can efficiently collect, process, and analyze data gain a significant competitive advantage. If you want to design and build data infrastructure, develop scalable data solutions, or manage and analyze large datasets, this course will provide the essential skills you need. The possibilities for applying what you learn are endless. Investing in data engineering skills will bring tangible benefits to you and your organization:.
What's in it for you?
For Organizations: Say goodbye to manual workarounds. You get reliable data, stronger governance, and a solid foundation to deploy AI models and autonomous agents with complete confidence.
For You: You’ll sharpen high-demand skills and get direct, hands-on experience with modern tools, techniques, and architecture patterns powering today's AI systems.
Beyond the Theory
Data engineering is all about making sure information flows smoothly and securely between your systems, your models, and your AI agents.
Rather than just talking about abstract concepts, this program puts you to work with hands-on exercises. You'll dive into everything from traditional databases to unstructured data lakes and modern vector stores, as well as the pipelines connecting them. You’ll walk away with the practical tools needed to build a clean, sustainable data infrastructure ready to handle the next generation of AI-driven work.
This is not a zero-coding beginner program. Participants without prior programming experience may find the pace challenging.
Learning Objectives
- Understanding the core concepts of data engineering
- Gaining a strong foundation in ETL (Extract, Transform, Load) processes
- Ability to design and implement ETL processes
- Getting hands-on experience with widely used data engineering tools
- Learning to design, build, and maintain robust, scalable data pipelines
- Prioritize data quality and understand data provenance
- Ability to work with various data storage solutions
- Developing the skills to support data warehouses, machine learning models, and AI-driven applications
Instructor
Matthew Wooller
Matthew Wooller is a senior freelance Data and AI Engineer who works with organisations and enterprises to help build-out data capabilities, develop machine learning solutions and implement agentic AI deployments.
Wooller has over thirty years of experience working on data-centric projects with companies as diverse as MTV Networks Europe and Asia, the UKTV group, Sulake, Basware and Sanoma. In the past ten years, he has returned to consulting and worked with global brands across varied domains to deliver data and AI-led initiatives and instill a data-driven, evidence based mindset.
Data Engineers: The Unsung Heroes of the Modern Organization
webinar with Matthew Wooller
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