• Automated document management processes, modernizing a vendor platform to identify uploads, run associated models, launch asynchronous extraction, and write status notifications via streaming. • Leveraged machine learning and AI deep learning to enable engineering teams to efficiently search over 2M documents while reducing duplicate work, human error, and risk.• Directed negotiations with the managed service and vendor teams to support a large upgrade, navigating challenges with limited infrastructure to recover from a 2-week delay for on-time delivery. • Strengthened development and QA, as well as the performance and accuracy of deep learning AI models.

• Fostered a culture of accountability and skill development across commercial bank engineering teams, including training on streaming, microservices, data lakes, and producer and consumer responsibilities. • Overhauled enterprise data architecture through executing a producer/consumer model, introducing a data lake and warehouse, migrating 100% of data to the cloud, and implementing architectures with API's and Stream integrations. • Reviewed applications for each commercial subdivision and classified data for streaming, APIs, or data lakes. • Designed a new customer data management system, merging relational and NoSQL databases to enhance capacity and minimize user impact while devising custom solutions for data traversing issues.

• Shaped data architecture strategy by evaluating AWS Data Services, assessing migration costs from the mainframe operational data store to the cloud, and reporting findings to the CIO and AWS Head of Data Services. • Liaised with divisional architects, the resiliency team, and internal cloud engineers to align AWS services with critical requirements and capabilities, explore opportunities, and outline gaps for senior executives. • Enabled new capabilities, including cross-region data replication, expanded RDS databases, and a transition to open-source datastores while ensuring disk-level encryption

• Created and led the Service Definition Strategy for the Operational Data Store (ODS) on DB2/Mainframe, ensuring data services were highly available, reusable, and resilient. • Led team of 6 engineering leads that oversaw multiple software and data engineering teams, and aligned strategy across our real-time services.• Managed team of data modelers (contractors and associates, reviewed and approved all data models, made significant contributions to the Card Division Logical Data Model