Spencer Young

Designed and operated distributed batch and streaming pipelines to enable near real-time ML-ready datasets for ranking, pricing, search, and experimentation. Improved pipeline stability and SLA adherence through proactive monitoring and data quality controls, and reduced end-to-end processing time and infrastructure cost via performance optimization.

Improved scalability and reduced data latency by migrating warehouse-centric ETL to distributed Spark and event-driven processing for clickstream and transactional data. Built curated datasets and aggregations for analysts and data scientists and contributed to orchestration and scheduling improvements to increase reliability.

Designed dimensional data models and enterprise data marts to deliver consistent, high-quality analytics for business domains. Improved data freshness and query performance by optimizing ETL across multiple source systems and evolved warehouse pipelines to support increased data volume and velocity post-acquisition.

Designed a standardized, reusable SSIS-based ETL framework that reduced ETL runtime from 3 hours to 30 minutes. Improved analytics consistency and reporting by creating a new dimensional data model and increased developer productivity by automating staging pipeline generation and adding data quality and master data services.

Migrated reporting solutions to OLAP cube-based architectures to improve reporting performance and analytical flexibility. Developed governed reports and dashboards using SSRS and PowerPivot to support lending product analytics and operational decision-making.

Led data-driven systems for trading oversight, billing, risk management, and regulatory reporting over an 11-year tenure. Reengineered workflows to reduce client request cycle time by 15%, helped reduce inbound support volume by 35%, and built automation that eliminated the need for one full-time role (~$40K annual savings).

Earned a Bachelor of Science in Accounting from Bentley University.