Evan Mesa

Spearheaded the integration of backend infrastructures for Hulu and Disney+, optimizing data flow and streamlining subscriber data management. Engineered and automated Airflow DAGS and Harmony jobs for daily data extraction with Snowflake SQL, enhancing operational efficiency. Optimized and deployed internal data visualization queries using Airflow, driving data accessibility and decision-making.

Managed and optimized daily batch runs on Airflow clusters for data migration between ingestion layers, supporting inventory forecasting efforts. Developed observability metrics using Python to monitor and report on Airflow DAG performance, enhancing operational transparency. Deployed robust Airflow DAGS and Spark SQL queries within AWS to facilitate efficient data processing across cloud infrastr

Architected and implemented the entire backend for a dynamic system predicting NBA player stats for daily fantasy and prop betting. Collected and transformed 20 years of historical player and team data, preparing it for machine learning applications. Developed an automated data pipeline that ingests and processes new data daily, empowering subscribers with insights.

Engineered a predictive data pipeline and football betting algorithm using Python, Pandas, BeautifulSoup, and Scikit-Learn. Developed a web scraping system that aggregated data from multiple sources, transforming it into actionable datasets for regression analysis. Engineered 200 features, applying dimensionality reduction techniques to refine and tune the algorithm for optimal performance.

Developed a system for scraping inventory data from construction industry websites using BeautifulSoup and Selenium. Built an intuitive system to streamline the scraping and categorization process, improving team productivity and data accuracy.