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Report abuseData & BI Analyst | Analytics Engineering — Davao City, Philippines
I find what the numbers are hiding, then build the reporting that keeps it visible.
Most reports tell you what happened last month. My work answers what to change — where working capital is stuck, which products lose money once discounts are counted, which costs are drifting off budget. The pipelines underneath are how I make those answers trustworthy enough to act on.
View the full portfolio → glcapitan.github.io
Each repo ships with the architecture, the code, and the validation queries used to prove it works.
| Project | What it answers | Built with |
|---|---|---|
| Supply Chain Analytics | An estimated ~$95K in working capital identified as trapped in slow-moving stock | DuckDB · Parquet · Kimball · Power BI · 101 DAX measures |
| Retail Analytics on Microsoft Fabric | Ingestion, transformation and dimensional modeling automated, with real CI/CD between workspaces | Fabric · PySpark · T-SQL · Deployment Pipelines |
| Azure SQL → Lakehouse, with SCD2 History | What a customer's plan was last quarter — SCD2 verified: 509 silver → 509 gold → 500 current rows | ADF · Databricks · Delta Live Tables · Unity Catalog |
| Sales & Customer Performance | Margin turns negative above a 20% discount; 16.2% of products destroy $76.7K | Tableau · parameter-driven design |
| Financial Performance Dashboard | A manual monthly P&L cycle replaced by one date cell and a refresh | Excel · Power Query · Power Pivot · DAX |
| NYC Taxi Analytics Platform | A high-volume dataset kept current without a full reload each run | Fabric · Data Factory · SQL Warehouse · Power BI |
| Fintech Fraud Analytics | Which transaction types carry fraud, across 6.3M records | Python · PostgreSQL · Streamlit |
Primary — daily drivers Power BI · DAX & Power Query · SQL Server / T-SQL · Microsoft Fabric · Excel / VBA · Python
Working knowledge — used in shipped projects Azure Data Factory · Databricks · PySpark · Tableau · PostgreSQL · DuckDB · Streamlit · Git
Exploring Snowflake · dbt · Airflow · Delta Live Tables · Unity Catalog
Methods Kimball star schemas · SCD Type 1 & 2 · Medallion architecture · Watermark incremental loads · Data quality validation
2025 — present · Data & BI Analyst, self-employed — dashboards, data models and end-to-end platform builds Jan – Jun 2025 · Data Analyst Intern, EXCELHelpline — six months on a live client account, delivered in Power BI 2019 — 2025 · Teaching — seven years translating complex material for non-technical audiences 2018 — 2019 · Branch Operations Supervisor — inventory, expenses, liquidations and staff performance reporting 2015 — 2018 · Credit & Risk Analyst — exposure, collections behaviour, and the reporting behind lending decisions
Certifications — Full-Stack Data Analytics (EXCELHelpline) · Microsoft Fabric Data Engineer Associate DP-700 (in progress) · dbt Analytics Engineering (in progress)
Open to BI and analytics engineering roles, and to freelance work. Working across time zones.
End-to-end supply chain analytics: medallion lakehouse → Kimball star schema → Power BI dashboard with 101 DAX measures → executive briefing. Reframed leadership conversation from "stockouts" to "o…
Python 2
fraud-scoring analytics on 6.3M transactions — Python, PostgreSQL, Streamlit
Python
One-click Excel FP&A dashboard built with Power Query, Power Pivot & DAX — budget-vs-actual, prior-period, and prior-year reporting from a single input cell.
End-to-end analytics platform on Microsoft Fabric — metadata-driven pipelines, incremental loading, SQL Warehouse, semantic model, Power BI
End-to-end Microsoft Fabric analytics pipeline — medallion architecture (Bronze → Silver → Gold). Raw retail data is ingested to Bronze, cleaned and conformed in Silver via PySpark, and modeled as …
Jupyter Notebook 1
End-to-end Azure data platform — watermark-driven incremental ELT with Data Factory, a Databricks medallion lakehouse on ADLS Gen2 governed by Unity Catalog, and a Delta Live Tables gold layer with…
Jupyter Notebook
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