Data & Analytics Engineer
I build the pipelines, warehouses and visualizations that turn raw data into answers — and lately, the agents that let you just ask.
This is my professional site — and it is also the infrastructure it describes. Every application linked here runs on a single AWS EC2 instance I administer myself: the sites, the REST API, the visualizations, the analytics, and two warehouses built the same way — 9.2 million hourly weather observations, and every college football and NFL play since 2014 — each with a plain-English agent on top. Automated wherever it can be, and pointed at the things that interest me most — data engineering, the impact weather has on your body, football, and most importantly good food.
Expertise
Equipping decision-makers with the tools — and now the plain-English interfaces — to make sense of the data.
Designing and running relational and non-relational stores — PostgreSQL, MySQL, SQLite, MongoDB. Ingesting and transforming JSON, CSV, Parquet, XML and YAML, and using Python, SQL and Bash to optimize each stage of the ETL and EtLT process. Pipelines run unattended on AWS Lambda and EventBridge, land in a PostgreSQL instance I manage myself, and back themselves up to S3 nightly with alerting when a run fails.
Explore the REST API docsHarnessing Python and its standard and third-party libraries — Pandas, Polars, PySpark, SQLAlchemy — to build robust data models. Applying advanced SQL techniques (CTEs, window functions, CASE expressions) to answer the hard questions, and using the relevant statistical and aggregation methods to make valid inferences from the results.
Putting a language model in front of a warehouse without handing it the keys. An LLM agent reads the live schema and writes the PostgreSQL; a Model Context Protocol server executes it as a SELECT-only role behind a query guard and a statement timeout. The web tier holds no database credentials and builds no SQL, and a token budget that fails closed keeps a public demo from becoming an open tab. The same pattern now runs over two warehouses, weather and football, reusing one tested security model rather than inventing a second.
Try the SQL ExplorerShipping the work as software people can actually open. Flask, FastAPI, Streamlit, Dash and Bokeh applications self-hosted on a single EC2 instance behind nginx — TLS from Let's Encrypt and renewed on a timer, each app its own systemd unit with its own service user and memory ceiling. Every request is captured into PostgreSQL by an analytics pipeline and read back through a live dashboard.
Visit the data viz siteDo you share a passion for finding the answers to the hard questions? Everything below is running right now, and nearly all of the source is public.
See some of my recent workPortfolio
Two warehouses built the same way — ingest and validate the data, serve it read-only, then put a plain-English agent and a visual explorer on top — and a shelf of smaller builds, each a different stack on the same box.
Warehouse 01
9.2 million hourly observations from NOAA and Apple WeatherKit, loaded by unattended AWS pipelines into PostgreSQL on EC2.
Six interactive visualizations belowAsk a weather question in plain English. Gemini writes the PostgreSQL, an MCP server runs it read-only against 9.2 million rows, and the query is shown beside the chart.
Apple WeatherKit REST API into a self-managed PostgreSQL database on AWS EC2, on a four-hour sequence, orchestrated with Lambda and EventBridge.
Hourly observations for 112 US airport stations in a self-managed PostgreSQL instance on AWS EC2, refreshed nightly from NOAA's GHCNh parquet files by Lambda and EventBridge.
Need the data from the two PostgreSQL databases that drive the data visualizations? This REST API hands it to you as JSON, with interactive documentation and an OpenAPI spec.
Warehouse 02
Every FBS college football and NFL play since 2014, from ESPN, one row per play with the people identified by stable id.
Ask about any college football or NFL play since 2014. Gemini writes the SQL, a read-only MCP server per league runs it, and the query is shown with the answer.
Every play, filtered by league, side of the ball, team, player and outcome. A Dash app reading a DuckDB snapshot, with drill-down and CSV or Excel export.
ESPN's feed parsed into two fact tables over shared dimensions in PostgreSQL, mirrored to EC2 and split into per-league serving schemas, rebuilt weekly in season.
Also built
Smaller builds, each a different stack, each self-hosted on the same box.
A typing-rhythm coach for writing AI prompts: burst speed, thinking pauses, backspacing and spelling, from every keystroke's timing.
Full-stack web application built with Flask, Python and PostgreSQL — self-hosted on EC2 behind nginx.
Two desktop apps — one for note-taking, one for studying — built to aid in passing the PCPP1 certification exam.
If you would like to discuss any of these projects, have critical feedback, or need more information, please get in touch.
Get in touch with meData visualizations
Bokeh applications served straight from the database behind this site.
Accessing the Apple WeatherKit REST API, data is updated every four hours via an automated pipeline.
Historical and forecasted metrics side by side, to aid in discovery and analysis.
Utilizing the power of SQL window functions (LAG) to drive metric creation.
Rich historical data representations in a concise manner, to aid in pattern recognition.
The GeoViews and HoloViews libraries can help! Check out which locations across the United States are more prone to these barometric pressure driven events.
Creative statistical visualizations to analyze which areas across the US have the wildest weather swings.
Contact
I'm available for freelance work in the programming domain — no project too small. Pipelines and warehousing, analytics and modeling, or putting a language model in front of data you already have.
