Engineering Capabilities
Expertise
A structured view of the engineering capabilities behind the systems I build.
Data Engineering
Building reliable, scalable pipelines that move and transform data at production quality.
01Batch pipeline design and orchestration with Apache Airflow
02Real-time streaming architecture with Apache Kafka
03Python-based extraction, transformation, and loading
04Data lake and lakehouse design with Apache Iceberg and Delta Lake
05ELT pattern implementation and scheduling
06Schema evolution and pipeline reliability strategies
Analytics Engineering
Turning raw pipeline output into trusted, governed analytical assets.
01dbt modeling across raw, staging, and mart layers
02Metric definitions as code with data contracts
03Business logic governance and testing strategies
04Semantic layer design for governed metric access
05Data quality framework implementation
06Documentation-driven development with dbt docs
Data Platforms
Designing and operating the infrastructure layer that everything else depends on.
01PostgreSQL data warehouse architecture and optimisation
02ClickHouse OLAP design for sub-second analytical queries
03Multi-layer schema design and indexing strategies
04Platform reliability, monitoring, and alerting
05Storage format selection and trade-off analysis
06Cost and performance optimisation for analytical workloads
AI Systems
Building AI workflows that reason over governed data and close the feedback loop autonomously.
01LLM reasoning over structured data via semantic layers
02Retrieval-Augmented Generation for analytical context
03Autonomous analytics agents with LangChain
04Claude API integration for natural language data interfaces
05Anomaly detection and KPI monitoring automation
06Decision intelligence systems that eliminate manual reporting
Stack Philosophy
Every capability listed above exists in service of a single goal: turning raw data into trusted, governed, decision-ready intelligence. The engineering stack is a means. The outcome is autonomous, AI-native analytics.