Traveled and lived in National Forests and National Parks in the Pacific Northwest

- Architected and developed a cloud-native, real-time streaming pipeline that enriched marketing leads using third party APIs, scored them against a predictive model, and returned them to Salesforce- Designed and implemented a custom data versioning pattern that allows for instant cutovers of whole datasets, QA before go-live, and instant, reliable rollbacks. Implemented this solution as a custom Airflow wrapper for repeatable use in ELT DAGs-Advocated for the adoption of CI/CD best practices and completely transitioned a data engineering team to an almost complete commit-to-prod pipeline. Implemented IaC, health monitoring and alerting, post-deployment testing, test coverage analysis, semantic versioning, linting, pre-commit hooks, automated testing, automated release notes, an ironclad security/deployment approval process, push-button rollbacks, and an artifact repository- Composed a proposal to parallelize predictive model creation using the Dask Python package and Kubernetes in order to alleviate processing bottlenecks for a data science team at a financial trading company- Assessed the viability of Snowflake in Azure by forecasting cost, reviewing the functionality of the Python SDK, and benchmarking it's operational efficiency- Developed a Python utility for genomic data scientists to use in model creation pipelines which allows them to more easily share datasets with each other by leveraging BigQuery's snapshot and copy table functionality

- Designed, developed, and deployed the Predictive Behavioral Routingproduct’s data pipeline and managed the project from discovery throughadoption- Decreased new client implementation time from forty hours to two bydistilling custom logic into configuration- Developed data structures, workflows, reporting layers, and extracts tomeet requirements for reporting, invoicing, model-building, andvisualization- Promoted adoption by guiding data science, analytics, finance, and QAteams through acceptance testing and process integration- Deconstructed a monolithic artifact into independently deployablepackages increasing the stability of Mattersight’s data warehouse- Identified and remapped dependencies across applications- Implemented continuous integration and automated testing using Jenkins2.0, greatly reducing patches  and work effort required for deploymentswhile increasing deployment velocity- Disentangled data workflows and transitioned their management from SSISto a custom taskexecution engine written in Python- Adhered to an agile development framework in the creation of a datastoreto feed various applications with generic call information- Worked with stakeholders on custom data integration projects to identifyand solidify success criteria reducing development weeks by a factor ofthree- Developed a Python application to extract and present clients’Salesforce data to a machine learning classification product to meet anaggressive SLA

Lead a team of analysts responsible for data quality and go-live risk reviews

Assured and improved the quality of e-commerce product data to improve website quality, search engine optimization, and Google AdWords effectiveness