AI Engineer | Software Engineer (Python, Node, React, React Native)
Software Engineer 7 years experience.
Experience in consumer finance, e-commerce, digital agriculture, solar energy, medical consumer software. Company types ranging from seed-stage through fast-growth venture to public companies, with a variety of team sizes and cultures.
Base is a membership club oriented entirely around in-person, highly curated, and algorithmically matched dinners and experiences, with personal authenticity is the foundation.
* Owned development of mobile app, members' primary method if interacting with Base, and only method of booking dinners and experiences.
* Built and launched foundational features such as "Perks", "Connect", and "Awards", that added social networking, gamification, and premium partnerships to the platform.
* Used marketing tools to measure feature performance and member engagement.
* Directly diagnosed and fixed members' issues with app interactions such as login of booking.
* Responsible 24/7 for critical issues, including
* Cross-functionally worked with Member Success, Experiences.
* Worked directly with Founders and early employees brainstorm business and product, and with CTO to conceive and prioritize new features.
* Performed technical interviews for engineering candidates.
* Owned backend for entire user registration flow, built on Rails monolith and Postgres. Lead roadmap and SLAs development, consistently provided backend constraints into the product development process.
* Deployed twice-weekly using Spinnaker CI/CD pipeline on GitHub/Jenkins. On-call PagerDuty monthly.
* Lead backend implementation of LaunchDarkly 5-variant A/B test on highest tier subscription price, directly resulting in C-Suite decision to 3x its price, directly increasing primary input to customer LTV.
* Lead project to decrease erroneous KYC results percentage from ~25% to ~5% on a daily signup volume of ~10k users, by working with brokerage back-office and web/mobile engineers to iteratively define, backtest, and implement validation rules on ~20 registration input fields.
* Helped build Rails models and routes for new feature to allow purchase of 100+ stocks/ETFs by potential or current users. Resulted within weeks of launch in ~10MM total search volume, ~2MM+ page views, and ~$150 average per-user securities purchases.
* Tested backend-driven UI content or flow changes in development and staging environments.
* Cross-functional work included: daily collaboration with product management, web/mobile, data science; and frequently with brokerage, compliance, marketing/copy, QA, and adjacent engineering teams.
* Production responsibility included PagerDuty shifts, logs monitoring, NewRelic system performance diagnostics, Slack alerts, and Looker-based anomaly analysis.
* Profiled and stress tested Rails monolith using New Relic as part of CTO-designated team to assess improvement opportunities for Rails monolith. Team concluded the current app was sufficiently resilient.
* Team of five built and launched marketing features for the 2MM+ Sellers on the platform, including “Promoted Offers” allowing sellers to promote specific listings; using PHP7, sharded MySQL, and React/Redux, i18n internationalization.
* Launched features to selected user buckets using server-side feature-flags. Worked with Data Science and Product to define user buckets and experiment duration to avoid interference with other experiments.
* Tested start-to-end seller and buyer flow using carefully customized test accounts and feature-relevant purchase scenarios. Deployed code daily using custom-built “Deployinator” pipeline.
* Built product dashboard using Grafana and internal logs, to track post-launch engagement
Digital Agriculture. dedicated to creating technologies that transform field data into meaningful insights that help farmers sustainably enhance yield potential, improve efficiency, and manage their risk.
* Owned and expanded data pipeline built on AWS Kinesis, Python ETL, and Postgres warehouse with 200+ tables.
* Maintained staging and production AWS infrastructure on S3, EC2, Docker, and CloudWatch monitoring/alerting.
* Added ingests from 3rd party APIs including Mandrill, and guarded against schema proliferation.
* Tested ETL speed and resource load improvements by optimizing SQL statements and monitoring impact using CloudWatch, concluding that scaling challenges lay elsewhere.
* Used Spark and Scala to build message bus module and contribute to data science warehouse, as part of team effort to re-architected pipeline in anticipation of increased scale.
* Wrote and distributed analytics specification documents based on Product Managers requirements.
* As team grew from 5 to 12+, helped prioritize scaling challenges based on effort and severity, at Analytics VP’s request, and edited/reviewed Principal engineers’ proposal for data infrastructure upgrades.
