Data Engineering
Metadata-Driven ETL Framework
Config-over-code ingestion that scales to hundreds of sources.
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The Challenge
Every new data source meant hand-writing another bespoke pipeline — slow to build and a nightmare to maintain.
The Solution
Engineered a metadata-driven framework where new pipelines are declared as configuration. The engine handles ingestion, typing, quality checks, and loading generically.
Key Achievements
- Reduced new-pipeline delivery from days to hours
- Standardised quality and logging across every pipeline
- Made onboarding new sources a self-serve task for analysts
Lessons Learned
When the framework is good enough, engineers stop writing pipelines and start writing configuration — a step-change in throughput.
Let's build something intelligent together.
Whether you're modernising a data platform or bringing agentic AI into production, I'd love to hear what you're working on.