I turn repetitive pipeline development into reusable data platforms.
Hands-on Senior Data Engineer focused on configuration-driven ingestion, reliable transformation, testing, and repeatable deployment across cloud and on-prem environments.
Built a reusable Azure Data Factory framework that enabled teammates to create and modify pipelines through YAML configuration stored in Blob Storage.
Used by approximately five team members across 40–50 pipelines.
Designed and built a Python, dbt, DuckDB, and SQL Server platform to replace copy-and-modify delivery built around SSIS packages and stored procedures.
Used by the data platform team across approximately 30 pipelines, reducing new-source pipeline delivery from about one week to under one working day.
- Languages and modelling: Python, SQL, dbt
- Platforms and orchestration: Azure Data Factory, Databricks, Blob Storage
- Data stores and engines: SQL Server, DuckDB
- Configuration over copy-and-modify
- Reusable patterns that teammates can operate
- Standardized delivery instead of one-off implementation
- Design for maintainability and team adoption