Summary

I engineer the SQL and Python pipelines that lending and treasury forecasts run on.

Six years across J.P. Morgan, Bloomberg, and Canadian Banking, building the data pipelines, automation, and integrations that turn raw financial data into forecasts leadership can trust. Below are a few systems I’ve built in my own time.

Projects

Things I’ve built with data

RC151 Assistant app screenshot

RC151 Assistant

A Streamlit app that calculates world income in CAD for the CRA’s RC151 form, pulling live daily FX rates from the Bank of Canada’s API. Matches income dates to the correct market-open exchange rate and aggregates multi-year, multi-currency salary data into CAD totals.

PythonStreamlitPandasAPI Integration
Open the app ↗
Apple Music SQL analysis

Apple Music SQL Analysis

A PostgreSQL and Python pipeline that mines a 10,000-track Apple Music dataset using window functions, generated date series, and percentile-based outlier detection. A parameterized matplotlib script renders the results into matching light and dark chart assets, reading DB credentials from the environment.

SQLPostgreSQLPythonMatplotlib
View analysis ↗
IMDb dataset analysis in Tableau

IMDb Dataset Analysis

A PostgreSQL pipeline that explodes IMDb’s comma-packed genre strings into rows with UNNEST and STRING_TO_ARRAY, then joins across four linked datasets (titles, ratings, regional releases, country codes) to track genre and rating trends by language and region over time. Results feed a public Tableau dashboard.

SQLPostgreSQLTableau
View story ↗

Tools & Technologies

SQL
Tableau
Python (pandas)
dbt
Power Query / M
Power BI / DAX

Day to day I work with SQL Server and Python script building data pipelines and transformation logic across large mortgage, treasury and financial performance datasets.

Certifications