• Developed a brand-new User Acceptance Testing system for Business Intelligence dbt code changes from the ground up, including CI/CD, alerting, and a testing suite, decreasing system downtime by 20%.

• Designed and built a version-controlled system for deploying data observability Monte Carlo monitors with code. Conducted five training sessions for the data operations team, integrating iterative feedback. Implemented rigorous testing, pull request protocols, and CI/CD, collaborating with 38 contributors to enhance data reliability. Gave product feedback accepted by Monte Carlo into upgrades and roadmap.

• Implement cost-saving measures and workflow optimizations, leading to significant financial savings in compute costs year-over-year and heightened developer productivity. Led initiatives ensuring compliance and data security in personally identifiable information (PII) management.

• Successfully managed two interns, guiding them to deliver project demos and tools that are now integral to the organization's daily operations. Both interns accepted full-time roles,

• Build new systems and processes to optimize business analytics and software development using industry-standard tools such as Python, Airflow, AWS, Azure, Snowflake, dbt, GitHub Actions, and Monte Carlo.

• Instructed evening courses to classes of 35-40 undergrad and grad students in:

1) data structures and algorithms in Java, and

2) internet/web technologies, including full stack development and network protocols.

• Created and provided feedback on assignments, projects, and exams.

• Nominated for the President's Award for Excellence in Teaching.

• Built and deployed NoSQL big data products and solutions in Apache Airflow with cloud infrastructure and modern software tools.

• Ensured the Python ELT code for each dataset was well-designed and robust, and that the data were highly available and up-to-date.

• Planned around resource constraints (eg, CPU, disk, memory, network, server availability), monitored products to maintain healthy status, and discovered and repaired product bugs.

• Prepared data for analytical and operational uses with SQL and Python.

• Built Python ETL pipelines to pull together data from different source systems into a Google Cloud Storage data lake and automated processes with Jenkins, Kubernetes, and Docker.

• Integrated, consolidated, and cleansed data for Google Cloud Platform systems, and structured data for use in individual analytics applications with Google’s BigQuery data warehouse.