• Leading IT organization while driving data platform strategy and delivering solutions that enable foundational data platforms that align across the Bayer Crop Science that enable over $25B in net sales• Driving data solutions that transform product demand prediction, R&D product pipeline advancement, decision science, and digital farming solutions• Developing strategic partnerships with functional leadership, business units ensuring technical platforms are highly scalable, loosely coupled, low friction, and fit for purpose to enable business needs• Managing combined $30M budget for three main data engineering teams:Global Crop Science Data Warehouse - Google Big QueryNext generation server-less Data Mesh framework enabling both the benefits of a self-service data lake as well as a centralized Core data layer to harmonize all functional data across Crop Science• Data Warehouse contains over 3PB of data and 3 trillion records• Automation Ingestion Pipeline responsible for $8M in cost avoidance between over three years• 115% increase in user adoption within 2 years across all functional areasData and Analytics PlatformProviding platforms that enable Data Scientists to run ML and GenAI analytical models that are revolutionizing how advancement decisions are made in the product pipeline for agriculture• Leading Build of multi-cloud next-gen Data Science Ecosystem abstracting cloud engineering through APIs, SDK, and UI for exploration through sandbox, MLOps, and Data Science Lifecycle• Divisional decision and data analytics platform with a user community of over 3,000 data scientists that enables AI/ML Model Pipelines, Orchestration, and Connective DecisionsData Enablement PlatformsDeveloping data platforms that enable real-time data streaming, data management, and data discovery• Multi-cloud multi-datacenter distributed Kafka solutions for real-time data streaming• Metadata, Master Data, Reference Data, Data Quality, and Lineage

• Led data platform engineering team responsible for enterprise cloud solutions in AWS and GCP Platforms include Data Streaming, Metadata, Data Quality, Ontology, and ETL• Data streaming platform is responsible for producing billions of messages a year that enable reusability of data and supports the company’s data movement across all divisions• Engineering team's responsibilities are to build and maintain data solutions in the cloud using:• AWS and GCP cloud engineering• Java, Scala, NodeJS, ReactJS, Python development• Kafka, Talend, Informatica, Elasticsearch, Presto (Data Federation)

• Led team of Data Streaming, Metadata, Master Data, Data Quality, and Ontology enterprise platforms• Led creation of Nodejs AI/ML Virtual Assistant and Chatbot integrated with Metadata Platform• Created reusable Java Kafka producer and consumer framework with ability to produce from over 12 different data sources accelerating development and adoption of Kafka

• Led development team and provided solution architecture designs and conducted code and performance reviews for all ETL development within the Enterprise Data Warehouse• Created reusable factory-based templates for Informatica Power Center that reduced development times by 75% with a total cost savings of $6M

• Responsible for data modeling, performance tuning, and developing Oracle PL/SQL applications for the US Seeds and Traits Crop Data Warehouse (CDW) application• Development in PL/SQL, optimized database performance, and data quality issues that may cause schedule failures or prolong batch processing