Snowflake Data Engineering

About this course

Progress from data engineering foundations to AI-powered Snowflake, with hands-on SQL, dbt, Python and AWS, and three capstone projects.

Prerequisites: Open to graduates, working professionals and career switchers. Ask an expert if you are unsure.

14

Learning units

60

Topics covered

3

Projects

10

Tools

1. 1

   Module 1

   Introduction to Data Engineering

   • Data engineering foundations
2. 2

   Module 2

   SQL for Data Engineering

   • DDL, DML and DCL
   • Inner, outer, self and complex joins
   • Subqueries and CTEs
   • Window and analytical functions
   • Data transformations and validation
   • Data cleaning for pipelines
   • SQL performance tuning
3. 3

   Module 3

   Data Warehousing Concepts

   • OLTP vs OLAP
   • ETL vs ELT
   • Star and snowflake schema modelling
   • Fact and dimension tables
   • SCD Types 0, 1, 2 and 3
   • Data lake vs warehouse vs lakehouse
   • Data quality, reconciliation and governance basics
4. 4

   Module 4

   Snowflake Core Features

   • Storage, compute and services layers
   • Virtual warehouses and auto-scaling
   • Micro-partitioning and pruning
   • Time Travel, Fail-safe and Zero-copy Cloning
5. 5

   Module 5

   Data Ingestion in Snowflake

   • Internal and external stages
   • COPY INTO and Snowpipe auto-ingest
   • CSV, JSON, Parquet and Avro
   • VARIANT for semi-structured data
6. 6

   Module 6

   Streams and Tasks: CDC

   • Streams for change data capture
   • Incremental processing patterns
   • Scheduled and event-driven tasks
   • Pipeline monitoring and error handling
7. 7

   Module 7

   Snowflake Views

   • Standard, secure and materialized views
8. 8

   Module 8

   Security and DBA Concepts

   • Row and column level security policies
   • Role-based access control
   • Data masking
   • Dev, Test and Prod setup
9. 9

   Module 9

   dbt: ELT Framework

   • dbt fundamentals with Snowflake
   • View, table, incremental and ephemeral models
   • Incremental load patterns
   • Macros, Jinja templating and snapshots
   • Tests, data quality checks and lineage docs
   • Git and CI/CD for production pipelines
   • dbt run vs dbt build
10. 10

    Module 10

    AWS for Data Engineering

    • S3: data lake storage and external stages
    • EC2: compute for ingestion and orchestration
    • IAM: security and access control
    • SNS: notifications and alerting
    • SQS: messaging and decoupling
    • Lambda: serverless automation
    • CloudWatch: monitoring and logging
11. 11

    Module 11

    Python Basics

    • Python scripting for data engineering
12. 12

    Module 12

    Snowflake Cortex AI Agents

    • AI-ready data platforms on Snowflake
    • Semantic views
    • Cortex Search Service
    • Cortex AI Agent
    • Vector embeddings and similarity search
    • Model Context Protocol (MCP) integration
13. 13

    Module 13

    End-to-End Capstone Projects

    • Three capstone projects (listed below)
14. 14

    AI tools

    AI Tools for Data Engineers

    • ChatGPT and Claude for SQL, debugging and documentation
    • GitHub Copilot for dbt, Python and tests
    • Snowflake Copilot
    • AI resume tools
    • AI interview preparation
    • AI-assisted pipeline design and error diagnosis

Projects you will build

These are training projects. Please describe them honestly as training work on your resume and in interviews.

Tools and technologies

• Snowflake
• SQL
• dbt
• Python
• AWS
• Snowflake Cortex
• Streamlit
• Git
• GitHub Actions
• GitHub Copilot