About
I enjoy working at the intersection of data engineering, machine learning and analytics — building pipelines, models and stories that help teams make better decisions.
Data Engineer and Analyst with hands-on experience building production-grade ELT pipelines on Azure and AWS, implementing Kimball dimensional models with dbt Core, and developing machine learning models for business prediction problems. Focused on reliable, tested, cost-efficient data systems that turn raw data into trusted insights through professional Tableau Dashboards.
Background
My background spans production-grade ELT pipelines on Azure and AWS, dimensional data modelling with dbt Core using Kimball methodology, predictive modelling using Python and scikit-learn, and analytics storytelling with Tableau and Power BI. I've built and maintained AWS S3 → EMR → Redshift pipelines at nbn Australia supporting financial and reporting teams, delivered Snowflake ELT workflows at IBISWorld, and built a full Azure medallion architecture pipeline from scratch as a portfolio project.
I work end-to-end — understanding the business question, defining the grain and data model, designing the ELT flow, writing and testing transformation logic in dbt, building ML models where relevant, and communicating results clearly to stakeholders.
Education
Master of Analytics — RMIT University
Bachelor of Technology- Electrical and Electronics Engineering — APJ Abdul Kalam Technological University
Currently focused on
- Actively seeking Data Engineer roles in Melbourne where I can contribute to modern cloud data stacks and grow into MLOps and AI engineering.
- Deepening expertise in Azure SQL Database, Azure Data Factory, dbt Core, GitHub Actions CI/CD, and migrating legacy ETL logic from Alteryx to Python.
- Improving feature engineering & model tuning to lift predictive performance.
- Curating a clean, recruiter-friendly GitHub portfolio of real projects.