Hi guys, for this section, we just want to check the materials with dbt Fundamentals and also learn more details about related topics. Follow the process below to see what we can supplement here:
https://learn.getdbt.com/learn/course/dbt-fundamentals/who-is-an-analytics-engineer-20min/introduction-to-analytics-engineering?page=3
Step Snap 1: Analytics Engineer - The Rise of ELT & Cloud Data Warehousing 🚀
The emergence of the Analytics Engineer role is a direct result of the shift from ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform) and the widespread adoption of cloud-based data warehouses like Snowflake, BigQuery, and Redshift. This transition has fundamentally changed how data transformation is handled and has led to a natural splitting of responsibilities in data teams.
The Shift: ETL → ELT
- In the traditional ETL model, Data Engineers handled data extraction (E), transformation (T), and loading (L) before data entered the warehouse. This required extensive pre-processing outside the data warehouse.
- In the modern ELT model, raw data is first loaded (L) into the cloud data warehouse, and the transformation (T) happens inside the warehouse using SQL-based tools (e.g., dbt). This change allows more flexibility and scalability.
The Role Breakdown: Data Engineer vs. Analytics Engineer
- Data Engineer: Focuses on data pipelines and infrastructure, handling data extraction (E) and loading (L) while optimizing storage, access, and performance.
- Analytics Engineer: Specializes in data transformation (T) inside the warehouse, using SQL and modeling techniques to structure data for business intelligence (BI) and analytics.
Why Did This Happen?
- Cloud Data Warehouses became powerful enough to handle large-scale transformations efficiently.
- ELT reduces Data Engineer workload, allowing them to focus on infrastructure rather than data modeling.
- Business teams needed clean, analysis-ready data, leading to the rise of Analytics Engineers who bridge the gap between raw data and insights.
Key Takeaway
The Analytics Engineer role emerged as a result of ELT adoption, where transformation is now done post-load inside cloud-based warehouses. This divides data responsibilities—Data Engineers manage infrastructure and ingestion, while Analytics Engineers ensure data is structured and optimized for analysis.
Step Snap 2: Data Engineers VS Analytics Engineers VS Data Analysts
Just a screenshot and copy of what the differences/importance info are stated by the author, these are just clear definitions!