I build analytical solutions that turn raw data into reliable datasets, useful insights and decision-ready reporting.
My work sits at the intersection of data analytics, SQL, Python, analytics engineering and business intelligence. I am particularly interested in building well-structured data workflows: from ingestion and transformation through to data modelling, testing and visualisation.
- Insight & analytical analysis, with a focus on turning business questions into measurable outcomes.
- Interested in customer behaviour, financial data, operational performance and data-driven products.
- Developing deeper capabilities in analytics engineering, data warehousing and cloud data platforms.
| Area | Technologies |
|---|---|
| Programming & analysis | Python, Pandas, NumPy |
| Databases & SQL | PostgreSQL, SQL, DuckDB |
| Analytics engineering | dbt, dimensional modelling, data quality testing |
| Business intelligence | Power BI, data visualisation |
| Data platforms | Snowflake, BigQuery, Databricks |
| Development workflow | Git, GitHub |
My current development focus is dbt and cloud data platforms. The technologies listed reflect a mix of practical project experience and ongoing learning.
- Retail Analytics, Forecasting & Customer Intelligence — End-to-end retail analytics using a dimensional warehouse, SQL, Python, forecasting, predictive modelling, automated tests and Power BI.
- Databases and SQL: Cross-Platform Data Warehouse — Database design, dimensional modelling and cross-platform SQL implementation.
- Superstore Data Warehouse — A practical exploration of dimensional modelling and analytical database design.
- MeteoFlow — API to PostgreSQL — API ingestion and persistence in a relational database.
- London Planning Atlas — A geospatial analytical product combining Python data pipelines, Parquet, DuckDB, FastAPI and an interactive web application.
- Market Intelligence Analysis — Market data ingestion, sentiment analysis, event studies, time-aware machine learning and an interactive dashboard.
- dbt and analytics engineering — modular SQL transformations, tests, documentation and deployment workflows.
- Cloud data warehousing — developing practical experience with Snowflake and evaluating BigQuery.
- Lakehouse architecture — understanding Databricks, Spark and Delta Lake.
- Data engineering foundations — reliable ingestion, orchestration, data quality and reproducible pipelines.
I value clear data models, traceable transformations, reproducible analysis and honest evaluation. A strong analytical result should be explainable, testable and useful to the people making decisions with it.
Good data explains what happened. Well-engineered data helps people decide what to do next.