I work best where data systems, teaching, and communication overlap. Small scope first. Clear deliverables. Hands on when useful.
01 Data systems
- Data pipelines, analytics engineering, and internal data products
- dbt, Airflow, Python, SQL, BigQuery, and pragmatic DataOps
- Platform audits, reliability improvements, and ML pipeline support
- Architecture review, implementation work, debugging, and cleanup
02 Workshops
- Apache Airflow for data and ML teams
- dbt and analytics engineering
- DataOps, observability, and production practices
- Python for engineering teams
- Technical writing and public speaking for engineers
03 Technical writing
- Tutorials, blog posts, and developer documentation
- Product demos, prototypes, and conference material
- Editing and reframing complex technical content
- Developer advocacy support for data and infrastructure products
04 Embedded and edge-ish work
- Embedded-adjacent prototypes
- Data collection near hardware
- Small systems where Python, Go, Linux, and practical constraints meet
- Enough hands-on work to avoid turning every problem into architecture theater