CF

Analytics Engineering for Data Professionals

12h 46m 13s
English
Paid
Updated September 2026

Analytics Engineering for Data Professionals is a 9-lesson 12 hours 46 minutes self-paced course by Fabrizio Valentini, Mattia Brunelli. Analytics engineering sits between data engineering and analytics, handling most of the data lifecycle in between: loading sources, building warehouses, and connecting everything to BI tools.

Course facts

Lessons
9
Duration
12 hours 46 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Fabrizio Valentini, Mattia Brunelli
Price
Premium

Analytics engineering sits between data engineering and analytics, handling most of the data lifecycle in between: loading sources, building warehouses, and connecting everything to BI tools. Fabrizio Valentini and Mattia Brunelli teach that full path in one practical course.

What You'll Build

  • A modern data warehouse in Snowflake
  • Automated data loading from multiple sources using Fivetran connectors
  • An ELT process using DBT and SQL to clean and transform data
  • A connection to the Preset BI tool for visualization and analysis

Across 9 lessons, you build a complete data engineering product from raw data through to real visualizations, useful whether you're an analyst moving into engineering, a data scientist rounding out your skills, or a genuine beginner to the field. Some prior SQL and data analysis experience helps but isn't required.

Who teaches Analytics Engineering for Data Professionals?

Fabrizio Valentini

Fabrizio Valentini thumbnail

Fabrizio Valentini is a data engineer and educator focused on the analytics-engineering discipline — the bridge between raw data engineering and downstream business analytics, anchored on tools like dbt, Snowflake, and the modern data stack.

His CourseFlix listing carries Analytics Engineering for Data Professionals. Material is paid and aimed at data professionals working on the transformation layer between raw data warehouses and business intelligence consumption.

Mattia Brunelli

Mattia Brunelli thumbnail

Mattia Brunelli is a data engineer and educator focused on the analytics-engineering discipline — the bridge between raw data engineering and downstream business analytics, anchored on tools like dbt, Snowflake, and the modern data stack.

His CourseFlix listing carries Analytics Engineering for Data Professionals — covering the dbt-centric workflow that has reshaped how analytics teams build the transformation layer between raw data warehouses and BI consumption.

Material is paid and aimed at data professionals working on the modeling and transformation layer between data warehouses and downstream consumers. For broader data content, see CourseFlix's Data processing and analysis category page.

What lessons are included in Analytics Engineering for Data Professionals?

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#1: 001 Live session 1 Introduction + overview of Data Stack
All Course Lessons (9)
#Lesson TitleDurationAccess
1
001 Live session 1 Introduction + overview of Data Stack Demo
01:26:03
2
002 Live session 1 Introduction + overview of Data Stack- Shared screen with speaker view
01:26:03
3
003 Live Session 2 Data Warehouse in Snowflake and data ingestion using Fivetran
01:27:11
4
004 Live Session 2 Data Warehouse in Snowflake and data ingestion using Fivetran Recording
01:27:11
5
005 Live Session 3 Setting up Github and DBT
01:19:54
6
006 Live Session 3 Setting up Github and DBT- Shared screen with speaker view
01:19:54
7
007 Live Session 4 SQL pipelines in DBT
01:28:56
8
008 Live Session 4 SQL pipelines in DBT- Shared screen with speaker view
01:28:56
9
009 Live Session 5 creating dashboards in Preset
01:22:05
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Books

Read Book Analytics Engineering for Data Professionals

#TitleTypeOpen
1AE session 1 PDF
2AE session 2 PDF
3AE session 3 PDF
4AE session 4 PDF
5AE session 5 PDF

What courses are similar to Analytics Engineering for Data Professionals?

Frequently asked questions

What are the prerequisites for this course?
The course is designed for both beginners in analytics engineering and professionals like analysts and data scientists looking to enhance their data engineering skills. Familiarity with basic data concepts and some experience in data analysis or data science would be beneficial, but not mandatory. The course will guide participants through tools like Snowflake, Fivetran, DBT, and SQL, making it accessible for those new to these technologies.
What is the main project or outcome of the course?
Participants will build a comprehensive data engineering product, taking raw data through the entire lifecycle to create insightful visualizations. This involves setting up a data warehouse in Snowflake, automating data loading with Fivetran, transforming data using DBT and SQL, and finally, connecting it to a business intelligence tool like Preset for visualization. This project will enhance your portfolio and showcase the skills acquired during the course.
Who is the target audience for this course?
The course targets data professionals such as analysts and data scientists who wish to deepen their understanding of modern data engineering tools. It is also suitable for beginners in analytics engineering who are eager to learn how to create data warehouses and integrate business intelligence tools into their workflows.
How does the course depth compare to other data engineering courses?
This course provides a focused exploration of analytics engineering, emphasizing practical application through tools like Snowflake for data warehousing, Fivetran for data ingestion, and DBT for data transformation. Unlike some broader courses, it specifically targets the integration of these tools with business intelligence platforms like Preset, offering a specialized skill set for participants.
What specific tools will I learn to use in this course?
The course covers several key tools essential for analytics engineering. Participants will learn to use Snowflake for creating and managing a data warehouse, Fivetran for automating data loading from various sources, DBT and SQL for data transformation, and Preset for connecting and visualizing data in a business intelligence context.
What topics are not covered in this course?
The course does not delve into advanced data science algorithms or deep learning techniques. Its focus is on the data engineering aspects of analytics, specifically the construction and management of data pipelines and the integration of business intelligence tools. Participants seeking in-depth data science methodologies may need to pursue additional courses.
How much time should I expect to commit to this course?
The course consists of nine lessons, each covering different aspects of analytics engineering. Given the practical nature of the course, participants should be prepared to spend additional time outside the lessons working on the project and mastering the tools and techniques introduced. This commitment will ensure a thorough understanding and application of the skills taught.