I build data platforms that reason. Six years turning messy source systems into
foundations people trust — now working where data engineering meets AI.
The belief underneath everything: elegant simplicity beats complex perfection. A pipeline you can explain in two sentences will outlive a clever one you can't. I'd rather understand why something works from first principles than memorise that it does.
- 🪶 Simplicity is the hard part. Anyone can add. The skill is knowing what to remove.
- ⚡ Action teaches what planning can't. I learn more shipping a rough thing on Monday than designing the perfect one for a month.
- 🧱 First principles, always. When I hit a gap, I learn it from the foundation — not the tutorial.
- 🤝 Build for the next person. Readable beats clever. The system should explain itself.
I'm a Senior Data Engineer at Pets Choice, where I built a greenfield Snowflake lakehouse — medallion architecture, dbt, Apache Iceberg, and Azure Data Factory. Lately I've been pushing into the layer above the warehouse: making data accessible through natural language and agents — Cortex semantic layers, Document AI, RAG, and MCP-compatible access for LLM tooling.
Data Platform & Warehousing
Snowflake (Iceberg, Dynamic Tables, Snowpipe Streaming, Cortex) · Delta Lake · medallion architecture · Kimball dimensional modelling
Pipelines & Orchestration
Airflow · Azure Data Factory · dbt · Great Expectations · CI/CD for data (GitHub Actions, Azure DevOps)
Streaming & Big Data
Kafka · Spark (batch & Structured Streaming) · Azure Event Hubs · PySpark optimisation (partitioning, broadcast joins)
AI & LLM Systems
Snowflake Cortex · RAG pipelines · Document AI / intelligent extraction · MCP (Model Context Protocol) servers · vector search · prompt & context engineering · agentic tooling with Claude Code
Cloud & Infra
Azure (ADLS Gen2, Synapse, Event Hubs) · Docker · Terraform · cost & performance optimisation
Languages & Analytics
SQL (deep) · Python · Bash · Power BI
- 🏠
london.rent— a PropTech rental platform built on that infrastructure, with neighbourhood intelligence.
Agent architectures and evals · MLOps & deployment (Kubernetes, Terraform) · system design · the boundary where the semantic layer meets LLMs.
Pets Choice — Senior Data Engineer · greenfield Snowflake lakehouse, Cortex + Document AI + MCP Tenacium DC — Data Engineer Everest — Data Engineer (consumer appliances) · cut annual pipeline compute costs ~$10k via Spark optimisation Orkash — Data Analyst · Airflow, Kimball star schemas, ~100GB daily volumes MSc Data Science & Analytics, Cardiff University
Off the clock: Formula 1 (Hamilton's the GOAT, Forza Ferrari 🏎️), basketball, festivals and raves, and long walks where most of my actual thinking happens. The Weeknd on repeat.
