Senior Data Engineer focused on data infrastructure, semantic tooling, metadata systems, and AI-native architecture.

I currently work on internal tooling, automation systems, data workflows, and metadata-driven infrastructure within complex proprietary environments, particularly around Niagara-based building management systems (BMS), IoT integrations, sensor data, and operational platforms.

A large portion of my professional work is organisation-owned and private, so this profile primarily contains personal projects, research, experiments, and architectural exploration.

Exploring semantic storage and retrieval architectures for structured data using normalization, semantic partitioning, manifest-driven chunking, and deterministic rehydration.

Expanding cloud and distributed systems knowledge with a focus on scalable infrastructure and data-intensive applications.

Studying storage engines, indexing, partitioning, retrieval systems, and distributed systems concepts through ongoing work and reading.

• Semantic storage systems
• Metadata normalization
• Parser and extractor tooling
• AI-native infrastructure
• Retrieval and knowledge systems
• Local-first architecture
• Schema translation systems
• Data-intensive applications
• IoT and telemetry systems
• BMS and operational technology data

I’ve worked across:

• Internal platform tooling
• Niagara-based BMS environments
• IoT and telemetry integrations
• Parser and extractor systems
• Sensor metadata extraction and normalization
• Rule and condition evaluation systems
• AWS-based automation and workflows
• Schema translation and migration tooling
• Sensor classification and mapping workflows
• Data onboarding and operational infrastructure

My experience sits at the intersection of:

• data engineering,
• systems thinking,
• infrastructure,
• metadata,
• and AI-assisted workflows.

• Designing Data-Intensive Applications — Martin Kleppmann
• AWS Data Engineering Associate material
• Storage, retrieval, and semantic systems architecture
• AI and domain-specific language model concepts

A local-first semantic storage and retrieval architecture for large structured datasets.

Machine learning experiments around sensor metadata classification and categorisation workflows.

Data engineering and infrastructure-focused project involving ETL workflows, PostgreSQL, and cloud services.

I enjoy building systems that make complex or messy data more structured, queryable, understandable, and operationally useful.