Building governed data models, semantic layers, forecasting systems, and BI for a Canadian fintech and payments company.

• Own the analytics semantic layer: KPI definitions and governed business logic for revenue, transaction, merchant, and profitability reporting, plus the executive dashboards leadership uses to track performance.
• Design and maintain layered dbt models on BigQuery and Snowflake that integrate Salesforce, HubSpot, NetSuite, and Kafka payment events into analytics-ready reporting tables with documented grains, tests, and lineage.
• Led the Tableau-to-Looker migration of 200+ reports, building a governed LookML semantic layer, improving critical report load times by 85%+ and reducing Tableau Cloud storage by 30%.
• Automated recurring finance, revenue, and executive reporting, reducing manual effort by over 90%.
• Partnered with FP&A to productionize a revenue and transaction forecasting pipeline in Python, SQL, dbt, and Airflow, containerized with Docker and Kubernetes, with 97-98% forecast accuracy.
• Built bank partner scorecards and presented findings to the C-suite steering committee to inform partner decisions.
• Built an internal LLM-powered assistant over company data, delivered through Slack, reducing ad hoc data requests by over 50%.
• Owned data validation and logic standardization through the AWS-to-GCP migration, reconciling migrated datasets and reporting outputs.
• Hold the quality bar with automated dbt tests, reconciliation frameworks, lineage, and SOX-aligned controls, including audit reports used by external auditors.
• Monitor pipeline health and expensive BigQuery workloads in Datadog and Grafana, and led Metabase adoption plus Looker training across business teams.

I teach data analytics and data science courses at SAIT, including SQL, Python, forecasting, and BI.

• Lead classes in advanced SQL, Python, R, statistics, forecasting, machine learning, data modeling, Power BI, Tableau, and Azure.
• Run hands-on coding sessions and mentor students through structured feedback on analysis and code.
• Design projects covering financial analysis, forecasting, Monte Carlo simulation, scenario modeling, anomaly detection, and KPI reporting.

Online STEM platform for Alberta Chemistry 20 and 30, with lessons, practice exams, and virtual labs.

• Founded and operate the platform with self-paced courses, practice exams, and virtual labs, owning product, content, pricing, and operations.
• Define and monitor KPIs across learner engagement, progress, assessment performance, and program performance.

Applied data science training covering machine learning, analytics, and project work.

• Built analytics and ML projects in Python, SQL, pandas, PySpark, scikit-learn, and XGBoost across classification, regression, clustering, and forecasting.
• Reached 85%+ model accuracy on selected projects through feature engineering, hyperparameter tuning, and cross-validation.

Analyzed clinical trial, genomics, and manufacturing data using statistical methods to identify trends, anomalies, and operational drivers.