Michael Sullivan

Owned the end-to-end data engineering pipeline for multi-state Extended Producer Responsibility (EPR) compliance reporting, sustainable packaging analytics, and regulatory data management across U.S. consumer products.

Designed and maintained enterprise ELT workflows processing 25M+ E-Commerce, retail, and theme park sales records, integrating sales, product, supplier, and packaging data from multiple enterprise sources using Snowflake, SQL, Python, and Snowpark.

Developed SQL transformation workflows and Python-based reporting scripts to standardize, transform, validate, reconcile, and prepare high-volume datasets for downstream reporting, analytics, and regulatory applications.

Built and maintained data ingestion and validation processes for packaging and sales data from 100+ suppliers, implementing data quality controls to identify missing, inconsistent, anomalous, and mismatched records before Snowflake ingestion.

Structured and loaded validated datasets into Snowflake using SQL-based ELT workflows, supporting centralized data warehousing, scalable downstream consumption, and audit-ready regulatory reporting.

Developed a Random Forest entity-resolution model using Python and Snowpark ML to automate retailer-to-internal product matching, reducing manual data reconciliation effort by 19%.

Designed automated Excel-based supplier data intake applications using dependent validation logic, advanced formulas, and error-checking controls, reducing manual processing effort by 50% while improving upstream data quality.

Translate complex regulatory and business requirements into scalable data structures, transformation logic, validation rules, data quality controls, and governed processes supporting multi-state compliance reporting.