Step Snap 1 [Introduction to Analytics Engineering]
1. The Rise of Analytics Engineering
The evolution of data engineering, cloud computing, and business intelligence (BI) tools has led to the emergence of a new role—Analytics Engineer—bridging the gap between data engineers and analysts.
- Cloud Data Warehouses (BigQuery, Snowflake, Redshift) reduced storage and compute costs.
- ETL & Data Integration Tools (FiveTran, Stitch) automated data movement.
- SQL-First BI Tools (Looker, Mode) introduced version control to analytics.
- Data Governance Tools enhanced security, privacy, and compliance.
Why is this role necessary?
- Data Engineers excel in infrastructure and pipelines but lack business context.
- Data Analysts & Scientists excel in data storytelling but lack software engineering best practices.
- Analytics Engineers bring software engineering principles (modularity, version control, testing) into analytics workflows.
Step Snap 2 [Key Responsibilities of an Analytics Engineer]
1. Bridging Data Engineering & Business Intelligence
Analytics Engineers translate raw data into structured, optimized, and accessible datasets for business users.
Key responsibilities:
- Data Loading & Storage:
- Managing ETL/ELT pipelines using tools like dbt (Data Build Tool).
- Ensuring efficient data modeling for fast querying.
- Data Transformation & Modeling:
- Applying Kimball’s Dimensional Modeling (Star Schema, Fact & Dimension tables).
- Writing optimized SQL transformations for faster data processing.
- Data Testing & Governance:
- Implementing CI/CD practices in data pipelines.
- Ensuring data quality with automated tests.
- Data Presentation & Self-Service Analytics:
- Developing BI dashboards (Google Data Studio, Looker).
- Enabling stakeholders to explore data independently.
Step Snap 3 [ETL vs ELT: Choosing the Right Approach]