Reston, VA
john@nevin.dev
(978) 973-6262
Education
Work Experience
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Member of the Trusted Postgres Architect (TPA) product team.
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Led development of an automatic file checksum validation feature to pinpoint troubleshooting of deployed clusters by comparing the TPA installations in the customer’s environment against the released package.
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Led migration of all CI/CD and integration testing Github Action workflows as part of the open-sourcing effort for TPA to enhance code security. This increased adoption of TPA by the community.
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Implemented support for TPA to deploy a novel and widely-adopted distributed Postgres cluster architectural pattern for active-active data replication and failover management.
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Led development of a feature for upgrading versions of all software components on deployed clusters.
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Developed Python applications to interface with third-party REST APIs to retrieve millions of events daily from many different sources and tools for forwarding, indexing and searching in Splunk.
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Incorporated robust logging into Python applications and wrote statistical searches for monitoring their performance and overall data quality in Splunk.
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Integrated Splunk and the application built on it with a SQL RDBMS and Elastic Stack Dashboard.
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Normalized, filtered, enriched and augmented terabytes of heterogenous data with fast, consistent and maintainable searches in Splunk.
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Worked to maintain 80% coverage of all data retrieved from disparate sources against the expected state and reduced downtime for reporting data downstream.
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Fully automated the deployment of Splunk app releases with a Python program; handles backing up, configuration and deployment with a user-friendly interactive CLI wizard.
Technical Skills
Personal Projects
A full stack application to build lifting workouts and analyze video to track bar-paths. Automatically schedules exercise days on a calendar, allows users to record sets and reps for each exercise and calculates the plates needed for the desired weight. Features collaborative form editing for building workout programs and video processing jobs using a messaging queue. My machine-learning model runs inference using a Dockerized AWS Lambda function to detect the barbell. The OpenCV Python daemon overlays the bar path. Users can view the processing status in real-time, and upon completion the video URL is provided.
A Progressive Web Application for reporting near-miss incidents between pedestrians/cyclists and motorists. Automatically records location of incident and allows users to easily provide details in dynamic forms. Thousands of map features can be displayed due to GPU-accelerated vector tile rendering. Web form questionnaires are built dynamically by parsing JSON. All data is stored locally in the browser IndexedDB for full-offline capability and user-ownership of their own data and sharing decisions.
Awards and Achievements
Authored ‘Representing Graphs in PostgreSQL with SQL/PGQ’ which became the most-viewed post of the month with over 7,500 hits. Gained traction and generated discussion on both YCombinator Hacker News and lobste.rs boards.
Received $4,000 in funding from the university to further develop my hackathon-winning project into a
minimum viable product in an eight week accelerator program.
Won a 24-hour Hackathon for creating a smartphone application for assisting individuals with memory loss that addressed the hackathon theme of “safety”.