Interactive, self-serve analytical tools built on Power BI, Tableau, Python, and R. Each synthesises data from multiple sources, stores it in cloud platforms such as Google BigQuery or Databricks, performs dimensional modelling to produce fact and dimension tables, and builds a semantic model in Power BI or Tableau, delivering well-designed visualisations that communicate clear insights for executive decisions.
Built an end-to-end data pipeline synthesising financial data from multiple sources (OSFI, Bank of Canada, StatsCan APIs) into Google BigQuery using Python ETL with scheduled refreshes on Google Cloud. From there, designed fact and dimension tables through dimensional modelling, queried with SQL, and fed the data into a Power BI semantic model with DAX measures, producing a scalable executive dashboard that gives insights regarding which financial institutions should be prioritized for assurance review based on emerging financial risk indicators.
Cloud Analytics | BigQuery · Power BI · Dimensional Modelling
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Watch Demo →Real-time energy market dashboard built on a StatsCan API pipeline. Features dynamic visualizations, automated data refresh, and an AI-powered insights generator that produces contextual analysis directly from live dashboard metrics.
Energy Intelligence | AI Insights
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Player Analytics
View Live →Interactive dashboard analyzing property performance, tenant trends, and operational metrics for real estate management decision-making.
Watch Demo →Interactive Tableau story analyzing global Airbnb market trends, pricing dynamics, geographic distribution, and listing behavior to uncover actionable insights for hosts and investors.
View Live →Interactive Power BI dashboard analyzing Spotify streaming performance and listening trends.
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