Modern data engineering does not happen in one tool. Real teams work across cloud infrastructure, orchestration, transformation, and analytics, and they need to debug fast when something breaks.

Join Snowflake and AWS for a hands-on Immersion Day built for technical practitioners who want to see how AI-assisted workflows can improve real data engineering work. In this session, you will use Cortex Code CLI to work across Snowflake and AWS as you inspect data, trace pipeline dependencies, diagnose a failure, fix a broken transformation, and validate the analytics outcome.

Anchored in a realistic e-commerce scenario, this workshop shows how modern engineering teams can move from issue detection to root-cause analysis and repair faster, without losing the rigour that production systems demand.

You will get practical experience with:

  • Snowflake for raw, staging, and mart data models using data in the interoperable Iceberg format
  • Amazon MWAA (Airflow) for pipeline orchestration
  • dbt for transformation logic and dependency management
  • Amazon QuickSight for analytics refresh
  • Cortex Code for guided investigation, debugging, and validation across the full workflow

This session will include a mix of theory and hands-on work. You will work through a lab, use the tools directly, and leave with a stronger understanding of how Snowflake and AWS fit together in modern data engineering.

What You Will Do

  • Build a medallion-architecture data pipeline on Snowflake (raw → staging → marts) using dbt, with data sourced from S3 Parquet files
  • Debug a pipeline failure using Cortex Code’s AI-assisted diagnostics, including tracing column lineage, identifying root causes, and fixing broken SQL without leaving the terminal
  • Deploy and orchestrate the pipeline with Amazon MWAA (Airflow), configuring DAG uploads, Airflow Variables, and end-to-end task monitoring
  • Publish results to Amazon QuickSight, triggering a SPICE dataset refresh from the pipeline and building a simple revenue dashboard
  • Experience AI-accelerated workflows first-hand: codebase exploration, data profiling, dbt execution, lineage tracing, and automated bug fixes, all driven by natural-language conversation with Cortex Code

Prerequisites

  • A laptop with a modern web browser (all lab work happens on a pre-provisioned EC2 instance – no local installs required)
  • Basic familiarity with SQL
  • No prior experience with terminals, Snowflake, dbt, Airflow, or Cortex Code is required – the labs are self-contained and guide you step by step

In collaboration with:

 

 

Modern data engineering does not happen in one tool. Real teams work across cloud infrastructure, orchestration, transformation, and analytics, and they need to debug fast when something breaks.

Join Snowflake and AWS for a hands-on Immersion Day built for technical practitioners who want to see how AI-assisted workflows can improve real data engineering work. In this session, you will use Cortex Code CLI to work across Snowflake and AWS as you inspect data, trace pipeline dependencies, diagnose a failure, fix a broken transformation, and validate the analytics outcome.

Anchored in a realistic e-commerce scenario, this workshop shows how modern engineering teams can move from issue detection to root-cause analysis and repair faster, without losing the rigour that production systems demand.

You will get practical experience with:

  • Snowflake for raw, staging, and mart data models using data in the interoperable Iceberg format
  • Amazon MWAA (Airflow) for pipeline orchestration
  • dbt for transformation logic and dependency management
  • Amazon QuickSight for analytics refresh
  • Cortex Code for guided investigation, debugging, and validation across the full workflow

This session will include a mix of theory and hands-on work. You will work through a lab, use the tools directly, and leave with a stronger understanding of how Snowflake and AWS fit together in modern data engineering.

What You Will Do

  • Build a medallion-architecture data pipeline on Snowflake (raw → staging → marts) using dbt, with data sourced from S3 Parquet files
  • Debug a pipeline failure using Cortex Code’s AI-assisted diagnostics, including tracing column lineage, identifying root causes, and fixing broken SQL without leaving the terminal
  • Deploy and orchestrate the pipeline with Amazon MWAA (Airflow), configuring DAG uploads, Airflow Variables, and end-to-end task monitoring
  • Publish results to Amazon QuickSight, triggering a SPICE dataset refresh from the pipeline and building a simple revenue dashboard
  • Experience AI-accelerated workflows first-hand: codebase exploration, data profiling, dbt execution, lineage tracing, and automated bug fixes, all driven by natural-language conversation with Cortex Code

Prerequisites

  • A laptop with a modern web browser (all lab work happens on a pre-provisioned EC2 instance – no local installs required)
  • Basic familiarity with SQL
  • No prior experience with terminals, Snowflake, dbt, Airflow, or Cortex Code is required – the labs are self-contained and guide you step by step

In collaboration with:

 

 

SAVE YOUR SPOT!

  • Dan Hunt

    Principal Partner Solution Engineer, Snowflake

  • Ankit Mathur

    Partner Solution Architect, AWS

Snowflake London Office, 1 Crown Place, London, EC2A 2FH

Date: May 20, 2026

Time: 02:00 PM BST