At Wells Fargo, I architect cloud-native data workflows across AWS, Azure, and GCP, bringing financial, customer, and operational data into governed analytical platforms. I’ve also established GenAI-ready data foundations for LLM, RAG, and agentic AI use cases.
At U.S. Bank, I led data engineering and cloud modernization work for risk, finance, and regulatory reporting. I built scalable pipelines with validation and lineage controls, and prepared governed datasets for predictive analytics and AI-enabled workloads.
At Ford Motor Company, I developed Java and Scala applications and data pipelines for connected-vehicle, manufacturing, and operational platforms. I also built Kafka streaming pipelines and migrated legacy batch workloads to AWS and Databricks; SQL-based data work improved accuracy and completeness to 90% across enterprise reporting datasets.
At Fidelity Investment, I developed distributed Big Data applications and streaming workflows for financial datasets. My work included Hadoop, Spark, PySpark, Kafka, and AWS-based processing.
