Data engineer focused on reliable data pipelines, clean data models and practical analytics workflows.
I work across SQL, dbt, Snowflake, Azure Data Factory and Python, with an interest in maintainable data transformation patterns, analytics engineering and practical use of developer tooling.
Local-first data pipeline using Python, DuckDB and dbt.
Silver award-winning hackathon prototype using natural language to identify available clinical data points and surface data quality information.
| Date | Course / Workshop | Focus | Evidence |
|---|---|---|---|
| May 2025 | Snowflake Healthcare AI / Machine Learning Workshop | Cortex AI, Document AI, Cortex Search, Cortex Analyst, notebooks and Streamlit applications | Repo |
| Apr 2026 | Snowflake Dynamic Tables Lab | Declarative pipelines, target lag, refresh modes and SQL-based pipeline design | Repo |
| May 2026 | Snowflake Intelligence Lab | Cortex AI, Cortex Search, Cortex Analyst and governed analytical AI applications | Repo |
| May 2026 | Snowflake AWS Immersion Day | Cortex Code, practical developer tooling and Snowflake data engineering workflows | Repo |
| May 2026 | Snowflake Open Pipelines Learning Reference | Apache Iceberg, Snowflake OpenFlow, Open Catalog, Apache Polaris, dbt Projects and multi-engine lakehouse patterns | Repo |
| May 2026 | dbt Fundamentals | dbt project structure, models, sources, tests and analytics engineering concepts | Repo |
- Reliable data pipelines
- Clean transformation layers
- Data modelling
- Analytics engineering
- Practical developer tooling
Repository names use labels such as project, learning-project, lab-reference, workshop-reference and learning-reference to distinguish original portfolio work from guided labs, workshops, course material and archived learning references.
Older archived repositories show earlier C#/.NET and SQL Server/SSIS experience, while newer repositories focus on data engineering, analytics engineering, Snowflake, dbt and Python.