Haince Pen

Defined and implemented architecture for shared AWS data pipelines and Snowflake data products, standardizing ingestion contracts and quality gates. Built metadata-driven ingestion framework and reusable Terraform modules, reducing provisioning effort by 60%.

Owned production batch and incremental data pipelines for healthcare pricing and pharmacy datasets using Python, SQL, AWS, and Snowflake. Reduced Spark job runtimes by 35% and automated API-based partner ingestion, reducing manual intervention by 30%.

Developed AWS-native ETL pipelines with Python, SQL, S3, Glue, and Redshift. Implemented incremental extraction patterns and optimized processing, cutting runtimes by 25%.

Designed integration architecture for utility datasets, establishing reusable ingestion patterns. Standardized failure handling and reduced recovery effort by 30%.

Engineered production data pipelines for utility usage and billing datasets. Reduced transformation runtimes by 30% and introduced automated data quality checks.

Implemented enterprise data governance practices including metadata management and data quality accountability. Developed lineage documentation and data classification rules.

Built Python and SQL ETL pipelines for utility data, implementing source normalization and data quality checks. Designed analytical tables and supported production operations.

Developed Python and SQL pipelines to consolidate enterprise infrastructure data. Automated reconciliation and data quality checks, and improved query efficiency.

Assisted with exploratory analysis of telecommunications network data using Python and SQL. Supported batch data preparation and created data checks.

Grade: 3.73

Master of Science in Information Sciences from Rochester Institute of Technology, completed in 2016.

Grade: 3.73

Master of Science in Information Sciences from Rochester Institute of Technology, completed from 2014 to 2016.