Data Engineering
ETL/ELT pipelines, data modelling, transformation, validation and analytics-ready datasets.
Developer · Data Practitioner · Builder
I build data pipelines, automation platforms and AI-powered applications with a focus on reliability, clarity and useful outcomes.
01 / Perspective
My work sits at the intersection of software engineering, data engineering and applied AI: Building practical systems that move information from raw inputs to decisions and useful experiences.
02 / About
From backend systems to analytics and intelligent applications, I enjoy solving problems where data, engineering and product meet.
I am a developer and data practitioner focused on building dependable software around data.
My projects span data pipelines, databases, APIs, cloud infrastructure, automation and AI-assisted workflows. I care about clean architecture, reproducible processes, data quality and systems that are straightforward to operate.
03 / Capabilities
A practical toolkit built around data, backend engineering, automation and modern AI workflows.
ETL/ELT pipelines, data modelling, transformation, validation and analytics-ready datasets.
Reliable services and integrations designed around clear interfaces, databases and automation.
Cloud-native data workflows with infrastructure that is repeatable, observable and maintainable.
Practical AI systems combining multimodal models, retrieval, computer vision and automation.
04 / Selected work
A selection of engineering and data projects demonstrating systems thinking, automation and applied AI.
Data Engineering / Cloud
Cloud-based data architecture using object storage, cataloguing and SQL analytics for scalable data workflows.
Data / AI / Backend
ETL, search and AI-assisted retrieval over healthcare facility data, exposed through a production-oriented API.
Automation / APIs
An automated workflow for transcribing podcast audio using OpenAI and HuggingFace APIs, with a backend API for retrieval and search.
05 / Journal
Notes on data engineering, software, AI and the process of turning technical ideas into working systems.
DATA ENGINEERING · 01
Why data quality, observability and reproducibility belong in the pipeline from the beginning.
Read article ↗AI · 02
Thinking beyond model inference: validation, confidence, structured outputs and production workflows.
Read article ↗ENGINEERING · 03
A practical look at connecting ingestion, storage, processing, APIs and the people who use the result.
Read article ↗06 / Contact
Let's talk about data, software, AI or an interesting problem that needs a practical solution.