Resume
Summary
Software engineer with 10+ years of experience building cloud-based systems, scalable data platforms, web applications, and AI-powered solutions, and scientific computing systems. Strong backend in integrating AI with data and software engineering to turn complex problems into practical and production-ready solutions, with end-to-end ownership from architecture and development through deployment and operations.
Tech Skills
Work History
Architect and lead development of 20+ full-stack, API, and data systems supporting energy research across national laboratories, spanning REST/gRPC/GraphQL, distributed systems, and cloud operations on AWS, GCP, and Azure. Built and operate a cloud portal managing 160+ projects/year — asset management, cost reporting, vulnerability alerting, patching, and compliance.
Develop and operate large-scale cloud data lakes, including OEDI and Data Foundry, providing petabyte-scale data lakes across AWS, GCP, and Azure. Build Python packages and data services with Trino and Airflow for scalable ETL and analytics across heterogeneous formats (Parquet, HDF5), backing a Redshift warehouse with 2+ billion solar time-series records.
Design and develop LLM-powered applications and AI agents using vector databases, RAG, and MCP to support scientific research, documentation, and knowledge discovery. Built AI chatbots and agentic systems connecting users with organizational knowledge, data stores, and tools.
Architect, implement, and maintain cloud software, APIs, and data systems for renewable energy across their full life cycle. Develop CI/CD solutions that improve scientific computing efficiency and reduce operational costs in an R&D environment.
Develop resilient energy modeling systems across cloud and HPC environments, including open-source Python software for orchestrating large-scale modeling jobs. Leverage Django, Celery, AWS Batch, and ECS to manage distributed computations supporting energy planning for stakeholders and programs across the US and internationally.
Provide technical leadership across multidisciplinary teams, partnering with scientists, researchers, and stakeholders to drive architecture, engineering standards, and operational practices. Mentor junior and mid-level developers through code review and adoption of modern software, cloud, and AI engineering practices.
Designed and built scalable AWS data-lake pipelines integrating agriculture, environment, weather, and hydrology data, performing terabyte-scale ETL and geospatial processing with Python, Docker, GDAL, and Postgres. Developed standardized ingestion workflows enabling R&D teams to efficiently analyze spatial-temporal datasets.
Developed reusable Python analytics packages and Django/GeoDjango REST APIs for programmatic access to curated datasets. Designed data models and supporting services enabling data scientists to discover, query, and analyze datasets through consistent interfaces and notebook workflows.
Established production-oriented engineering practices for scientific data products, including testing, Sphinx documentation, GitLab CI/CD, and Artifactory-based release management. Automated build, test, and deployment workflows to improve reliability and maintainability of agri-science software.
Partnered closely with data scientists and domain experts to translate scientific requirements into scalable engineering solutions. Provided technical guidance across ingestion, ETL, geospatial analysis, and deployment, helping accelerate development of data-driven precision-agriculture applications.
Designed a real-time environmental monitoring and anomaly-detection system for groundwater and air quality in northern Colorado, an area of intense oil and gas activity. Architected it on AWS (EC2, RDS, S3) with Django, Channels, and Celery on the backend, and JavaScript, Chart.js, and Leaflet for interactive, location-aware visualization.
Engineered scalable data pipelines for continuous sensor-data ingestion, transformation, QA/QC, and storage from distributed monitoring networks. Designed Postgres/PostGIS models and async workflows for time-series and geospatial data, and developed statistical/ML algorithms with Python/PyData to detect environmental anomalies.
Worked across software engineering, data science, and environmental research to translate monitoring requirements into production systems, connecting sensor data with cloud-based analytics and decision-support tools. Contributed across architecture, full-stack development, testing, and operations for applications serving the oil and gas industry.
Developed web-based decision-support systems for oil-gas and environmental applications, building mathematical models for produced-water analysis and cost optimization with Django, PostgreSQL, and PyData. Built interactive geospatial interfaces with Esri's ArcGIS API, Highcharts, and CreateJS; one research achievement was licensed by CSU to a private Fort Collins company.
Contributed to the Colorado Water Watch program, developing a pilot IoT-based real-time groundwater-monitoring app for the Wattenberg Field. Integrated surrogate sensing and time-series data with ASP.NET MVC, SQL Server, and Highcharts to deliver near-real-time information to researchers, regulators, industry, and the public.
Developed mathematical, statistical, and ML methods for environmental anomaly detection, combining groundwater observations, spatial-temporal data, and transport simulations to identify contamination events. Translated research methods into production-oriented software, laying the foundation for later work in real-time event detection and online research services.
Developed Python algorithms and geospatial data-processing tools for geoprocessing, NLP, spatial data mining, and location-based analytics, contributing to Business Expert — a geo-business intelligence product suite adopted by a broad range of clients.
Designed data pipelines for multi-source geospatial integration, ETL, and spatial analysis using Python, PostgreSQL, and the ArcGIS platform. Developed reusable workflows that improved research efficiency and strengthened the company's large-scale data mining capabilities.