Why ELT Became Critical

The role of ELT in a cloud world:

Traditional ETL (Extract, Transform, Load) evolved into ELT, where transformation happens inside the cloud warehouse.

• Data unification - bringing together SaaS tools, APIs, and legacy systems
• Cleansing & validation – fixing inconsistencies and duplicates
• Standardised transformations – shared business logic and metric definitions
• Scalability & automation – pipelines that scale to billions of rows and run on schedule

In a landscape full of “plug and play” tools, ELT keeps data accurate, governed, and usable at scale.

Why Data Governance Matters

As more people gained access to powerful platforms, the risk of misuse or misinterpretation grew.

Governance is now essential to:

• Define ownership and stewardship (who is responsible for which data)
• Standardise definitions and metadata (what does “active user” really mean?)
• Protect sensitive data (role-based access, anonymisation)
• Ensure auditability and compliance (lineage, regulatory reporting)
• Build trust in self-service (catalogues and semantic layers)

The Shift That Sparked the Role

Analytics Engineering emerged as a direct response to the challenges of the cloud era:

• Cloud platforms decoupled storage and compute - making data accessible, but not necessarily usable.
• Explosion of SaaS data sources – inconsistent definitions and silos, but demand for a “360° view”.
• Rise of self-service BI – empowering users, but creating conflicting dashboards and metrics.
• Evolution of ELT with dbt and orchestration tools – applying software engineering principles to analytics.

This created the need for a new role: someone to own the transformation layer, enforce standards, and build reliable, scalable data models.

Why Analytics Engineering Is Critical