Data Analyst | Analytics Engineering · Python · SQL · Power BI
I build analytics solutions that move from raw data to decision-ready outputs: reproducible pipelines, dimensional models, validated analysis, management KPIs, dashboards and data-driven applications.
My portfolio spans financial analytics, analytics engineering, SQL, Power BI, time-series forecasting, machine learning and OCR/NLP application development.
End-to-end financial analytics engineering project for synthetic fee monitoring, reconciliation and revenue-leakage analysis. The project models the full path from transaction generation through PostgreSQL transformations and dimensional modelling to BI-ready datasets and Power BI measures.
Python · SQL · PostgreSQL · Power BI · dimensional modelling · ETL/ELT · data quality · pytest · GitHub Actions · Docker
Portfolio highlights: deterministic synthetic financial data, finance-safe fee calculations, staging/intermediate transformation layers, star-schema dimensions and facts, management KPI marts, SQL quality checks, Power BI-ready exports, DAX measure definitions and automated CI.
End-to-end OCR/NLP web application built with Flask and Google Cloud Vision. It validates image uploads, extracts handwritten text, reports OCR confidence, identifies keywords and returns related search results through a responsive frontend.
Python · Flask · Google Cloud Vision · OCR · NLP · Docker · pytest · GitHub Actions
Portfolio highlights: automated API tests, CI, secure cloud-credential handling, production Gunicorn/Docker configuration and a Render deployment blueprint.
Time-series forecasting project investigating long-term global biodiversity trends. The analysis uses walk-forward validation, compares ARIMA against a naive baseline and publishes prediction intervals rather than presenting a single deterministic long-range forecast.
Python · pandas · statsmodels · ARIMA · time series · model validation · Tableau
Portfolio highlights: selected ARIMA (4, 1, 0) achieved RMSE 0.000846 versus 0.003221 for the naive baseline — about a 73.7% improvement in the project validation — with the point forecast reaching the 0.50 threshold in 2133.
Two analytics case studies covering COVID-19 exploration and Nashville housing data cleaning. Corrected portfolio SQL is separated from the original exploratory work and paired with reproducible Python-generated summaries and charts.
SQL · MySQL · Python · pandas · CTEs · window functions · data cleaning · GitHub Actions
Portfolio highlights: joins, CTEs, window functions, views, duplicate handling, normalization and recruiter-friendly result outputs generated from the committed project data.
Sports-analytics and regression project studying how player attributes relate to overall rating across goalkeeper, defence, midfield and attack roles.
Python · pandas · scikit-learn · regression · feature engineering · pytest · GitHub Actions
Portfolio highlights: the historical notebook is complemented by a portable tested pipeline with safer parsing, position-specific regression, MAE/RMSE/R² evaluation and support for the identified FIFA 21 raw-data schema. Concrete model outputs remain intentionally dependent on restoring the source CSV rather than fabricating results.
Analytics & BI: SQL, Power BI, Tableau, Excel, KPI definition, dimensional modelling
Data Engineering: Python, pandas, PostgreSQL, MySQL, ETL/ELT, data quality, reproducible pipelines
Modelling: scikit-learn, statsmodels, regression, ARIMA, time-series validation
Applications: Flask, REST/API workflows, Google Cloud Vision, OCR/NLP
Engineering: Git, GitHub Actions, pytest, Docker, Gunicorn
I structure portfolio work around a business or analytical question, make transformations reproducible, define metrics explicitly, validate models against meaningful baselines, test reusable logic and expose results in a form that another analyst or stakeholder can use.
Earlier coursework and smaller exercises are preserved in archived repositories. The projects above are the work I recommend reviewing first.
For analytics engineering, financial analytics and BI, start with Financial Fee Analytics Platform.
For an end-to-end application, see Document Digitalization.
For statistical modelling and validation, see Biodiversity ARIMA Forecast.
For SQL and data-cleaning work, see SQL Data Analytics Portfolio.