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Advanced dbt

dbt has been a breakthrough tool in the analytics industry, enabling analytics professional to leverage software engineering best practices and build robust data transformation and modelling infrastructure–but if you’ve been using dbt for a while, then you probably know, it can come with challenges. This course builds on the fundamentals of "Analytics Engineering with dbt" and aims to address common challenges when scaling teams or projects using dbt. We’ll cover methods to help you automate your workflows, common use cases for macros and packages, learn considerations and best practices for scaling your testing strategy, and identify ways to set yourself up for success as you grow your team and scale your work with dbt.

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Lindsay Murphy
Head of Data at Secoda
US$ 400
or included with membership
4 weeks
Space is limited

Course taught by expert instructors

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Lindsay Murphy

Head of Data at Secoda

Lindsay Murphy is a data leader with 11 years of industry experience. She recently joined Secoda as their Head of Data. Previously Lindsay was Director of Data and Analytics at Maple, a Toronto-based virtual healthcare startup.. At Maple, Lindsay focused on scaling the data team and its capabilities, increasing efficiency and growing its dbt project. Prior to Maple, Lindsay launched the Business Intelligence and Analytics practice at another Toronto-based start-up named BenchSci, where she built out a team and modern data stack infrastructure (Stitch, BigQuery, Heap, and Google Data Studio).

The course

Learn and apply skills with real-world projects.

Who is it for?
  • Analytics Engineers or Data Analysts looking to advance their dbt skills

  • Data team leaders (VP, Director, Manager, or Team Lead) who are trying to improve team efficiency and developer happiness

  • Other members from outside the data team (Product Analysts, Project Managers, Software Engineers) who want to better understand data team workflows

  • Experienced with SQL: you've been writing SQL for a few years and have experience with concepts such as CTEs, window functions, subqueries, etc.

  • Experience with dbt OR completion of other Uplimit course, Analytics Engineering with dbt: you've been writing dbt for at least 6 months and are familiar with all of the basic foundational concepts (models, tests, macros, Jinja, packages, snapshots, etc.)

  • Experience with cloud data warehouses: you've worked with common data warehouses such as Snowflake, BigQuery, Redshift, Databricks, etc.

  • Experience with command line and git: you've been writing in the command line and using Git for at least 6 months, are familiar with basic git commands, and the overall concept of version controlling your dbt code base.

Not ready?

Try these prep courses first

  • Common challenges that arise when trying to scale dbt projects
  • Best practices for scaling dbt projects, workflows, and documentation
  • Inherit a dbt project for a growing subscription business
  • Use dbt_project_evaluator to assess the project's health and resolve issues
  • Leverage dbt doc blocks to create a single source of truth for scalable documentation
  • Methods for driving more efficiency while using dbt
  • Common use cases and applications for Jinja, macros, snapshots, and packages
  • How to automate tedious workflow steps (dbt codegen package, pre commit hooks, etc.)
  • Practice building custom macros for more advanced use cases
  • Format Jinja for cleaner compiled code
  • Leverage helpful dbt packages to speed up your code development processes
  • Install pre commit hooks to enforce project quality and conventions
  • Methods to improve testing efficiency and data quality management
  • Additional data quality tooling and packages for your infrastructure
  • How to build dbt unit tests and when to use them
  • Testing strategies for scaling your dbt project
  • Identify and correct areas of test bloat and redundancy
  • Build custom generic tests and unit tests
  • Implement additional data quality monitoring packages
  • How to optimize project deployment as you scale (Slim CI, source fresher)
  • How to leverage advanced materializations to improve model performance
  • Methods for optimizing and containing the cost of running your dbt project
  • How to easily manage software updates for your team
  • Build an advanced incremental model
  • Refactor a poorly performing model
  • Add packages to help you monitor and optimize costs

A course you'll actually complete. AI-powered learning that drives results.

AI-powered learning

Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.

Live courses by leading experts

Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.

Practice on the cutting edge

Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.

Flexible schedule for busy professionals

We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.


Completion certificates

Each course comes with a certificate for learners to add to their resume.

Best-in-class outcomes

15-20x engagement compared to async courses

Support & accountability

You are never alone, we provide support throughout the course.

Get reimbursed by your company

More than half of learners get their Courses and Memberships reimbursed by their company.

Hundreds of companies have dedicated L&D and education budgets that have covered the costs.


Frequently Asked Questions

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