I am an enthusiastic and inquisitive Data Engineer (formerly an Architect in training 🏠) — I have the most fun when figuring out solutions to complex challenges, writing efficient code, and driving decision-making through stories hidden in data. Checkout my LinkedIn for more information!

Though some of the work was privately developed for clients, here is a list of some of the public work I collaborated on:

• packages/codegen: Contributed to dbt codegen’s generate_model_yaml macro by creating jinja statements and for loops that allowed the package to generate yaml files for multiple models at once.
• packages/dbt_project_evaluator: Enhanced compatibility of the dbt_project_evaluator package with Windows OS by creating a macro with over 25 lines of code that detects a computer’s operating system to provide appropriate model directory recommendations.
• internal_analytics/model-config-maintenance: Reviewed Internal Analytics Repository and updated 125 model configurations and their folder structures to standardize model materialization and adhere to dbt best practices.

Collaborated on the design, implementation, and development of a full data stack for a non-profit organization, consisting of Airbyte, BigQuery, dbt core, and Looker.

• blog_post/first_blog_post: 🏗️ To be released 🏗️

Designed a full data stack for Fantasy Football league data, consisting of python ingestion scripts, Airflow, BigQuery, dbt core, and Hex.

• python_script/sleeper_data_tapper: This python script accepts a list of Fantasy Football Sleeper App League IDs receives a API response as a JSON file. The script then converts the JSON file into a local CSV file that can be loaded into a Data Warehouse.