Professional Overview
Data platform engineer. I build ETL pipelines, Airflow DAGs, and exception-handling systems. I've built three production-grade Python platforms from scratch: DataFlow: 13-module data platform. Ingestion, cleaning, mapping, validation, lineage, reconciliation, ETL/ELT with watermark-based incremental loads and CDC. 3,830 passing tests. Workflow Exception Handler: deadline-tracking and exception-resolution engine with escalation, multi-level HMAC approvals, and hash-chained audit. 413 tests. Runbook Generator: incident response backend with AI RCA, live observability binding, and governed execution. 280 tests. Stack: Python, Airflow, SQL, pandas, Docker, FastAPI, Postgres/SQLite, REST APIs, CI/CD. How I work: I use AI-assisted development. It means I deliver at the pace of a small team at a solo contractor's rate, and every line is tested before it ships. Full test suites, golden-file regression fixtures, and documented architecture are standard on everything I build. Open to freelance contracts and fixed-scope projects. Remote, async-friendly. GitHub: [https://github.com/autoflowcompliance]
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
