Portfolio
Data Engineering & Automation
Data Visualization & Analysis
Web Development
Personal
Developed an automated data pipeline for a local art center to process event attendance, social post metrics, paid social campaign performance, website traffic, and email campaign engagement. Using Python-based API scripts, along with Google Cloud Run, Cloud Scheduler, and BigQuery, the pipeline provides live data to a Looker Studio dashboard for the client.
This solution replaced a cumbersome manual data collection and reporting process involving 11 different platforms, cutting out 10+ hours of manual work each month and enabling the center to visualize data in ways that were previously unavailable.
Developed an automated data pipeline for a realtor association to aggregate housing market metrics, census data, population and migration trends, and other economic datapoints into a single view. Using Python-based containerized API scripts, along with Google Cloud Run, Cloud Scheduler, and BigQuery, the pipeline provides live data to a Looker Studio dashboard for the client and association members.
This solution replaced a series of large monthly PDF reports that utilized static charts, enabling the association to visualize and interact with data affecting all aspects of the real estate market in ways that were previously unavailable.
Developed an automated data pipeline for a major university health system to process marketing and call data from multiple platform sources. The Python-based pipeline retrieves, stores, and aggregates data in a SQL database and connects to a Tableau analytics dashboard for use by marketing executives each month.
This automation replaced a manual monthly data export and upload process from each platform, streamlining reporting and improving overall data accuracy. It reduced the data refresh time from approximately 15 hours to a five-minute automated script run each month.
Developed a containerized Python application and automated reporting process, that leverages AI technology to analyze and categorize over 2,000 social media posts per day from 12 platforms based on message content.
This automation replaced a time-consuming manual process that took team members 2-3 hours per day0 sorting and analyzing each post to determine its sentiment and topic, significantly improving efficiency and accuracy while reducing the margin for human error. The final categorized reports are output to a structured Google Doc.
Zone In Columbus was a city-wide initiative to modernize Columbus's zoning code while empowering the community to shape the future of city development.
An automated data pipeline was developed that first transformed unstructured JSON survey responses from a MongoDB data lake into Google BigQuery, and a live visual dashboard was built for the Zone In team and the public to analyze survey results in real time. The dashboard was featured at the Zone In public gallery, where individuals could learn more about the zoning code proposal in an interactive environment.
Developed a self-serve ETL tool for both internal and client use that allows users to connect to several platforms and to customize data extracts and destinations. Jobs can be set up as a one-time export or as a scheduled job configured by the user.
This web-based app utilizes Flask to handle API routing and interactions with a Firebase database. It supports authentication with each platform, allows users to save their data configurations, and provides a friendly interface for non-technical users to set up and manage their own data pipelines with minimal technical support. The tool is currently in use by several clients and internal teams, and has replaced the need to set up individual scripts and pipelines for each client, streamlining repetitive data tasks for both the internal team and clients.
Developed a Flask-based web app that allows users to log in with their Spotify credentials and explore their listening habits. The app displays users' most-played artists and tracks, along with insights into track popularity, release dates, and artist follower counts. Users can create playlists, share their listening data on social media, and create custom dashboards.
InTune integrates with the Spotify API to fetch real-time listening data.
Currently developing a Flask-based web application using the hidden ESPN API to give fans and fantasy baseball managers quick and easy team and player stats, along with league standings, scores, and recent news.