AI-Native DataOps for Analytics Engineers

Build reliable, scalable analytics workflows that teams can develop, test, deploy, and operate together. Learn modern DataOps practices across Git, CI/CD, environments, testing, observability, and AI-assisted team workflows.
Length & format

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

Why take this course?

Move beyond individual development workflows and learn how analytics engineering teams ship changes safely and consistently. Build practical DataOps habits for collaboration, automation, quality, and reliable delivery, with AI embedded into the engineering workflow.

Who is this for?

Analytics engineers, data engineers, analytics developers, and technical leads working in team-based analytics environments who want stronger engineering practices around development and deployment.

What is expected from you?

You are comfortable working with SQL and modern analytics workflows and have basic familiarity with Git. Experience with dbt or a similar transformation framework is helpful.

What you will learn?

  • Use Git workflows that support effective team collaboration
  • Build CI/CD pipelines for automated testing and deployment
  • Design development, test, and production environment workflows
  • Apply testing and quality checks throughout the delivery lifecycle
  • Use observability and operational signals to identify and resolve issues
  • Integrate AI assistants into development, review, debugging, and operational workflows
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Build the skills your data team needs to develop and operate analytics systems reliably at scale. Our experts teach practical DataOps workflows grounded in the realities of modern analytics engineering teams.

Hands-on learning

Work through realistic team workflows covering version control, automated testing, CI/CD, deployment, and operational troubleshooting.

Learn from experts

Our instructors are analytics and data practitioners who build and operate production data platforms and understand what reliable team workflows look like in practice.

Built for modern data teams

Learn engineering practices that help teams move faster without sacrificing quality, from collaborative development and automated delivery to AI-assisted operations

Meet your instructors

Simo Tumelius

Co-founder, Lead instructor
Started in software engineering, where test-driven development and clean code shaped his approach.
Then transitioned into data, focusing on making analytics systems scalable, tested, and reliable.
Simo discovered dbt in 2019 and recognized its potential early on.
In early 2021, he went full-time freelance, helped teams adopt dbt and build sustainable analytics practices through mentoring, coaching, and training.
He has taught teams across Europe, the U.S., and India, with focus on helping people truly understand and confidently use modern data tools.
His training materials, built over five years, now form the foundation of Breakout Labs' dbt Foundational and Advanced Analytics Engineering programs

Miguel Carvalho

DATA Engineer, instructor
Started in software engineering before moving into data engineering, where he focuses on building reliable and well-structured data platforms.
Has worked across consulting, small companies, and now at a large tech company, giving him experience working in very different data environments.
He works extensively with Snowflake, dbt, and modern ELT tools to design and maintain production-grade pipelines, with a strong focus on consistent metrics and clean, scalable data models.
Alongside his hands-on work, Miguel delivers training in Foundational and Advanced Analytics Engineering, with an emphasis on practical approaches that teams can adopt straight away.
His focus is on helping teams build confidence and autonomy in their data stack.