Analytics Engineering is one of the fastest-growing roles in data, as it acts as the bridge between raw information and the trusted, analysis-ready datasets teams rely on every day. This path takes you from the fundamentals all the way to building an end-to-end data pipeline using tools from the modern data stack.

We'll start by unpacking what analytics engineering is and how modern data teams talk about it (OLTP vs. OLAP, ETL vs. ELT, etc.), then shift into data modeling concepts, where you'll design fact and dimension tables, build star and snowflake schemas, and handle slowly changing dimensions (SCD).

From there, we'll get hands-on with Snowflake, one of the most widely used cloud data warehouses. You'll explore its architecture, write SQL using Snowflake-specific functions, and use built-in features like Time Travel, Zero-Copy Cloning, and Cortex AI.

Next, we'll bring dbt into the mix to transform raw data into clean, reusable models. You'll build a real dbt project from scratch, including writing tests, generating documentation, and applying staging vs. mart conventions.

Finally, we'll tie everything together with Airflow, orchestrating a complete end-to-end data pipeline. We'll be ingesting data with Python, transforming it in dbt, storing it in Snowflake, and visualizing the results in Tableau, all while automating every step with Airflow.

By the end of this path, you'll be able perform the role of a real-world analytics engineer, taking raw data and transforming it into analytics-ready models for data analysts, data scientists, and BI developers to use for analysis and decision-making.