Email: charles@charlesreid1.com
Website: https://charlesreid1.com
Github: https://github.com/charlesreid1
Data engineer with experience building bioinformatics data pipelines for cloud systems. Comprehensive understanding of data engineering infrastructure, including networking, security, scalability, tools, and services. Teaching experience in mathematics and computer science; experience with industrial data engineering and computational combustion from chemical engineer position at fast-growing oil & gas startup (Siluria Technologies) and national lab engineering postdoc (Lawrence Livermore National Laboratory).
Excellent analysis, problem solving, communication, and writing abilities.
Seeks out challenging problems and iterates rapidly towards better solutions.
Breaks down complex issues to their essential components, extracting insight.
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Unix Command Line Tools: git, vim, bash (expert); comfortable with grep, sed, awk, regular expressions
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Languages: Python (primary language), SQL, shell scripting, Javascript, Java
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Databases: BigQuery, SQL (MySQL, Postgresql, sqliite), NoSQL (MongoDB, redis)
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Cloud Platforms: AWS, Google Cloud, automated infrastructure deployment, scaling
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Data Engineering: Docker, Kubernetes, Spark, Beam, Kafka, OAuth
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APIs: use of Github/Google/AWS/Twitter APIs, deployment of API servers
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Software Engineering: utilize object-oriented programming, test-driven development, and continuous integration
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Project Management Infrastructure: Expertise building, deploying, and maintaining static and dynamic web pages to support project management infrastructure; experience with Heroku and Github Pages; Linux DevOps experience
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Professional Certifications: Google Cloud Certified Data Engineer (2017)
- Built a search engine tool (centillion) to index Google Drive documents, Github issues, email threads, and other project material https://github.com/dcppc/centillion
- Built and maintained testing infrastructure to run continuous integration tests on Github pull requests https://github.com/dcppc/uncle-archie
- Contributed over 700 issues and pull requests during 6-month period (Phase 1) https://github.com/dcppc/contributions
- Supported development of bioinformatics data and computation pipelines for cloud/HPC platforms https://github.com/dahak-metagenomics/dahak
- Advised clients on migrating data/computational infrastructure to the cloud and scaling up
- Planned, executed, automated, and maintained cloud infrastructure for clients
- Handled cloud architecture, cloud networking, and security testing for both proprietary and encrypted data
- Taught data structures and algorithms (Java) to students in West Seattle preparing to apply as transfer students to the University of Washington
- Incorporated computational tools and assignments into calculus classes
- Provided undecided STEM students with advice about career paths, explanations of different branches of engineering and science, and captivating applications of mathematics, programming, and engineering
- Mentored students on NSF-funded undergraduate research projects on wireless sensor networks (Docker, NoSQL, wireless sensors, networking technologies), cybersecurity, and facial recognition with neural networks
- Automated data collection and analysis workflows with Python and SQL databases to improve turnaround time for pilot scale ethylene-to-gasoline reactor from 3 weeks to 24 hours
- Built Python scripts to control laboratory equipment and automate experiments
- Participated in design & assembly of sensor network and data acquisition system for pilot scale carbon dioxide separation unit
- Informed critical operational and safety decisions with statistical analysis of thousands of autoignition simulations
- Improved reactor scale-up correlations by over 50% through side project with colleague
Engineering Postdoctoral Researcher, Lawrence Livermore National Laboratory (October 2011 - April 2013)
- Applied pobabilistic methods for black box model validation of UCG (underground coal gasification)
- Analyzed geological data to inform site selection for Eurasian UCG project
- Ran composite grid wind flow simulations for wind farm optimization project
- Dissertation topic: statistical model reduction and quantitative model evaluation, multiphase turbulent combustion, massively parallel simulations, coal gasification.
- Final dissertation parameter study utilized 13,000,000 CPU-hours on the Hopper supercomputer at NERSC.
- Awarded College of Engineering's Outstanding TA Award (2010).
- Awarded Department of Chemical Engineering's Best Senior Design Project Award (2007).