AI-Ready Data Products

Design data products that people, applications, and AI systems can understand, trust, and reuse. Learn how to combine strong data modeling with clear semantics, quality controls, documentation, ownership, and governance.
Length & format

2-day instructor-led training (virtual or on-site)

Why take this course?

Move beyond building technically correct data models and learn how to create data products that remain understandable, reliable, and reusable over time. Develop practical methods for making structure, meaning, quality, ownership, and usage explicit so both human consumers and AI-enabled applications can use data with confidence.

Who is this for?

Analytics engineers, data engineers, technical product owners, data architects, and other practitioners responsible for designing or implementing reusable data products.

What is expected from you?

You are comfortable with SQL and have practical experience with data modeling or analytics engineering. Familiarity with dbt or a similar transformation framework is helpful.

What you will learn?
  • Translate business use cases into clear data product boundaries and design decisions
  • Apply data modeling practices that support reuse and semantic consistency
  • Define meaningful data quality expectations, tests, and contracts
  • Create documentation and metadata that make data understandable to people and AI systems
  • Establish ownership, governance, and lifecycle practices for dependable data products
  • Assess and improve the readiness of existing data products for analytics and AI use cases
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Build the skills your data team needs to create data products that can be trusted and reused across the organization. Our experts teach practical product thinking and engineering practices grounded in modern analytics environments.

Hands-on learning

Work through realistic data product scenarios, making decisions about structure, semantics, contracts, tests, documentation, ownership, and change management.

Learn from experts

Our instructors are analytics and data practitioners who design and build production data products and understand the trade-offs between usability, reliability, governance, and speed.

Built for modern data teams

Learn how to build data products for a world where data is consumed not only through dashboards and applications, but increasingly by AI assistants, agents, and automated systems.

Meet your instructors

Marvin Geerken

Co-founder, Lead instructor
Started in e-commerce before finding his path in data.
After earning a master's in Business Intelligence and Analytics, he joined Scalefree, a consultancy specializing in modern, large-scale enterprise data platforms.
He became a core expert in dbt, Snowflake, and Data Vault - developing automations and leading implementations.
After building a dbt + Data Vault training program, his passion for enablement was sparked, he then went freelance in 2023 to seek independence and greater impact.
Marvin connected with Simo around a shared vision, leading to the founding of Breakout Labs.
He then continues to support enterprise clients like Siemens while co-leading Breakout Labs' training content and delivery.

Samuele Crescenti

instructor
Began his data career in Bologna after studying Computer Science at the University of Bologna, working on early data warehousing and analytics projects.
Afterwards, he moved to Sydney where he worked in data consulting, designing and delivering data platforms across industries.
He then joined Canva as a Senior Data Engineer and later Tech Engineering Lead, working on large-scale analytics pipelines powered by dbt and Snowflake.
He had helped grow the analytics engineering practice and mentored engineers as the team expanded across Canva.
Samuel returned to Bologna, continued to build modern data platforms as a Data Engineer specialising in dbt and Snowflake
He now teaches dbt to help teams adopt analytics engineering best practices and build reliable, scalable data workflows.