Christopher

Created an automated DAG creator to efficiently onboard new healthcare partners and create new data pipelines without having to rely on boilerplate code for all DAGs. This decreased the complexity of creating data pipelines and allowed newer hires to work on data pipelines.

• Vendor onboarding using GCP, Airflow, and Python.

Created robust data pipelines using PySpark to clean, aggregate, and enrich data. Worked with epidemiologists to enhance analysis and convert legacy SAS code into PySpark jobs.  Automated a previously manual process by setting up a data connection that calls CSV data from an API, parses/processes the data, and then transforms it into a PySpark data frame.

• COVID-19 response and modernization using PySpark, Palantir Foundry, and SAS
• Created monitoring that alerts users if there are discrepancies in the data and provides an overview of the day’s data intake.

Evaluated Google Cloud Composer as a potential tool to replace Talend as the main data pipeline tool for the company. Created a QA data pipeline that increased reliability and ease of use over existing Talend jobs.  Received approval from engineering management to rewrite 100+ Talend jobs into Composer data pipelines.

• Created an internal library of Python code to productionalize common tasks like deeply nested JSON parsing.
• Cloud Composer POC using GCP, Python, Airflow

Created analytics pipeline that also incorporated 3rd party sales data to produce a Tableau report that broke down sales by the mentioned categories. Categorized 85% of the available data using this method, which led to an uptick of 3% in sales for the specific product.

• Web and Text Scraping Analysis using GCP, Tableau, Python
• Used regex and the natural language toolkit Python library to categorize sales data based on user comments.

Led an India-based engineering team as a project manager for a Fortune 500 client. Gathered requirements from multiple stakeholders and coordinated the team to deliver multiple analytics projects.

• Created a random forest model to predict customer traffic at a zip code level, which was used to identify optimal locations for new stores.
• Onsite Project Manager using GCP, Tableau, Python

• Capital Markets Technology Intern at Regions Bank (2016)
• Operations Analyst Intern at Cortland Partners (2016)
• Financial Analyst Intern at PrimeRevenue (2016)
• Business Analyst Intern at T-Mobile (2015)