Skills Overview
Data Engineering & Pipelines
Charles builds production-grade data pipelines that ingest, transform, and validate data from multiple sources. As a Data Engineer I at Clearway Energy Group, he works daily with Dagster, dbt, Polars, and Snowflake to automate renewable energy asset data workflows. He has experience designing ETL/ELT processes that handle API integrations, spatial data joins, and incremental loading patterns.
Tools & Technologies: Python, Dagster, dbt, dlt, Polars, Snowflake, DuckDB, Hex
Demonstrated in: Renewable Asset Performance Pipeline, Housing Affordability & Commute Analysis, Google Analytics Data Archive, Energy Analytics Pipeline, Synthetic Signal Observatory
Statistical Analysis & Modeling
Charles applies rigorous statistical methods to answer research questions and validate findings. His work includes OLS regression with robust standard errors, ANOVA, interaction models, quantile regression, and cross-validation. He is comfortable selecting between model specifications using AIC, diagnosing multicollinearity with VIF, and interpreting results for both technical and non-technical audiences.
Tools & Technologies: Python (statsmodels, scipy), scikit-learn, R
Demonstrated in: Housing Affordability & Commute Analysis, Wine Quality Analysis, Ames Housing Price Prediction, Sleep Deprivation Analysis
Machine Learning
Charles has experience building and evaluating supervised learning models for both regression and classification tasks. He has worked with linear models, k-nearest neighbors, random forests, XGBoost, neural networks (TensorFlow/Keras), and stacking ensembles. He applies feature engineering, cross-validation, hyperparameter tuning, and model interpretability techniques like SHAP to understand and communicate model behavior.
Tools & Technologies: scikit-learn, TensorFlow/Keras, XGBoost, SHAP
Demonstrated in: Ames Housing Price Prediction, Spaceship Titanic Classification, Wine Quality Analysis, Housing Affordability & Commute Analysis
Data Visualization & Dashboards
Charles creates visualizations and interactive dashboards that make data accessible to decision-makers. He has built R Shiny dashboards with dynamic filtering and interactive tables, Tableau dashboards with parameterized views, and Python-based visualizations using Matplotlib and Streamlit. He focuses on clarity, appropriate chart selection, and making the story in the data easy to follow.
Tools & Technologies: Tableau, R Shiny, Matplotlib, Streamlit, ggplot2, Plotly
Demonstrated in: Global CO₂ Emissions Dashboard, National Parks Visitation Dashboard, World Happiness Dashboard, Synthetic Signal Observatory, Housing Affordability & Commute Analysis
SQL & Database Analytics
Charles writes SQL for data extraction, transformation, and analytical queries. He is comfortable with aggregations, window functions (RANK, ROW_NUMBER), CTEs, subqueries, joins, and set operations. He works with both traditional relational databases and modern analytical engines like DuckDB and Snowflake.
Tools & Technologies: SQL (MySQL, Snowflake, DuckDB)
Demonstrated in: Restaurant Sales Analysis, Baby Names Analysis, Motor Vehicle Thefts Analysis, Renewable Asset Performance Pipeline, Energy Analytics Pipeline
Spreadsheet & Business Analysis
Charles uses Excel as an analytical tool — not just for data entry, but for building structured analyses with pivot tables, VLOOKUP/SUMIFS, conditional formatting, Pareto charts, and heat maps. His spreadsheet work is focused on translating raw operational data into actionable business insights.
Tools & Technologies: Excel (Pivot Tables, Power Query, VLOOKUP, SUMIFS, Conditional Formatting, Combo Charts)
Demonstrated in: Manufacturing Downtime Analysis, NYC Collision Analysis
Data Wrangling & ETL
Charles regularly cleans, transforms, and integrates messy datasets from multiple sources. He works with tabular data, spatial data (shapefiles, GeoJSON), and API responses. He is experienced with both Pandas and Polars for dataframe operations, and GeoPandas for spatial joins and geographic analysis.
Tools & Technologies: Python (Pandas, Polars, GeoPandas), API integration, Census ACS, OpenStreetMap Overpass
Demonstrated in: Housing Affordability & Commute Analysis, Electricity Consumption Analysis, AirBnB Listing Analysis, Google Analytics Data Archive
Web Development
Charles built his portfolio website from scratch using vanilla HTML5, CSS3, and ES module JavaScript — no frameworks or bundlers. The site features responsive design, accessible markup (WCAG 2.1 AA), filterable project galleries with URL-driven filtering, and a testimonial carousel. He maintains the codebase with professional practices including linting, automated testing, and CI/CD deployment.
Tools & Technologies: HTML5, CSS3, JavaScript (ES Modules)
Demonstrated in: Portfolio Website
DevOps & Tooling
Charles uses modern development tooling to maintain code quality and automate workflows. He sets up CI/CD pipelines with GitHub Actions, writes tests with pytest and Jest, manages Python environments with uv, and enforces code standards with linters (ESLint, Stylelint, Ruff) and formatters (Prettier). He follows conventional commits and semantic versioning practices.
Tools & Technologies: Git, GitHub Actions, CI/CD, uv, pytest, Jest, Playwright, ESLint, Stylelint, Ruff, Prettier
Demonstrated in: Portfolio Website, Renewable Asset Performance Pipeline, Housing Affordability & Commute Analysis