● Architected and implemented backfill framework in Airflow & DocDb/Mongo, increasing observability and traceability on 1M+ (ongoing) data writes from 0 to 100% by implementing a verification system. Reduced development time of net new DAGs by 95%.

● Develop backend, event driven and Restful AWS services in Python utilizing Lambda, SQS, RDS, S3, Aurora, DynamoDb, CDK, Github Actions, EC2 and other technologies.

● Refactored an event streaming Lambda Consumer to write to S3 connected to Snowpipe and Snowflake, reducing infra cost from $18,000 to $14 yearly.

● Lead DevEx and observability practice, using tools such as Cloudwatch, Grafana, Loki, AWS Xray, Sentry, Tempo, Promethous and OpenTelemetry.

● Contribute Python production code and Observability to AI Projects, using Pinecone, LangChain & GPT.

● Developed net new service utilizing FastAPI, Docker, EC2 DynamoDB and Redis. Set up infrastructure layer utilizing Terraform. Set up CI/CD, including a testing step. Ensure reliability through 100% code coverage.

● Led technical planning and implementation for sandbox API, which enables users to more easily

explore and integrate with Oura's third party API. Utilized generic coding approach, reducing net new

code by 80%.

● Refactored existing partner API to allow for versioning and ensuring backwards compatibility. This

enabled release of enhanced tagging on the mobile app, with over 1M MAU and 600k+ DAU,

increasing engagement of this feature by 23%.

● Improved architectural standards and test coverage for newly added endpoints to Oura API.

Decomposed controller class through abstracting and decoupling methods, increasing code

coverage to over 90% and making it easier to write tests for newly added business logic.

● Contribute to webhooks repo, which utilizes AWS Lambda, CDK, RDS, DynamoDB, and SQS (pub

sub). Added code to improve logging and monitoring, reducing triage time for incidents by 25%

● Technical lead for team in charge of third-party financial API integrations, overseeing 7 internal engineers and 3 external engineers & working with 2 product managers.

● Established and oversaw technical planning process. Prior to this, my team completed 13 integrations in 2021 versus 16 integrations in q1 of 2022, representing a 400% increase in team productivity.

● Oversaw technical implementation & drive overall architecture. Through introducing better abstraction & object composition, reduced lines of code of main controller class by 33%, & simplified testing through ability to mock abstracted objects.

● Wrote production code with Typescript and Python for Node.JS and Django backend services.

● Participated in code pairing & code reviews, teaching junior engineers about best software practice.

● Contributed & participated in system observability and triaging utilizing DataDog, OpenSearch & other tools.

● Implemented SignalFX and logging for three microservices, raising performance monitoring by 95% for each service.

●Led backend architecture, feature implementation, client story planning and third party lab testing integration for HealthPass NBA release, a COVID screening tool enabling businesses and venues to screen people entering their space. This enabled our app to be used by several arenas across United States, screening upwards of thousands of fans/employees per game.

●Refactored API call to parse values from User Agent header, reducing client side code on Android and iOS for this endpoint by 40% on each platform.

●Contributed to iOS and Android codebases, working on implementing features, reviewing architecture and troubleshooting client/server issues.

●Triaged backend, client side and CI/CD issues utilizing tools such as Splunk, SignalFx, Embrace and Jenkins.

●Participated in code pairing and code reviews, teaching junior engineers about best software practices and onboarding of our codebase.

●Increased revenue for version 2.0 iOS app by 300% YOY with $83M in revenue in 2019 as Lead Engineer, reducing codebase by 40%.

●Increased test coverage by 70% as Data Engineer on Python Machine Learning Platform predicting jet engine failure. Utilized Pandas, Numpy, Flask, AWS S3 and PyTest. Contributed to 15+ jet engine saves, valued at $2.5M per save.

●Reduced testing time by 70% as Backend Engineer for platform in C# .Net through enabling port forwarding in kubernetes naming service. Worked with MySQL, PostgreSQL and Quartz Scheduler.

●Engineered & released first production app for accelerator, utilizing mvp design pattern & functional/protocol oriented programming, reducing codebase (compared to previous release) by 35%.

●Reduced 97% of codebase for module through utilizing UserDefaults and system libraries.

●Improved code quality of projects through implementing code reviews among multiple software engineers.