Career Profile
Backend Software Engineer. 9 years of industry experience. 2 years of research experience.
Currently working on the platform team at Iterable.
Experiences
User Data Infrastructure.
Adding Cockroach DB into our stack.
- Aggregation using ElasticSearch.
- Terraform, Kubernetes on EKS.
Platform Team.
Responsible for the performance, security, scalability of the Fieldwire cloud platform.
- Ruby on Rails, Postgres, CDK on AWS.
- Provionsioned Data Infrastructure - Athena, Redis, BigQuery.
- Managed Fieldwire API releases.
- Implemented defensive measures against faulty and malicious API clients.
- Integrated a more modern Stripe Invoicing systems.
- Improved Postgres database performance.
- Onboards new engineers and oncall rotations.
Commerce-Data Team.
Responsible for the financial reporting of Yelp’s revenue.
- Owners of revenue Python batches.
- Owners of revenue Data Warehouses.
- Owners of Data Pipeline applications.
- Owners of revenue Data Warehouses.
- Designed Spark-based ETLs reports.
- Devos with PaaSTA compute infrastrcutural.
- Oncall hero and incident response team.
- SOX Compliance experience.
Responsible for the end-to-end device Integration on the Works With SmartThings platform. Worked closely with cross-functional teams in different geographic regions:
- Bay Area (Design, Biz-dev)
- Bangalore (Development)
- Korea (Strategy)
- Poland (Quality)
Engineering a distributed, service-oriented architecture for connecting IoT devices to the SmartThings cloud. Built a real-time video streaming platform on the SmartThings OpenPlatform. Outreach to the developer community with events and coding demonstrations.
- Video streaming platform on Node.js (Hub and Cloud)
- Device health monitoring with Kafka
Built a demand-response energy platform used by IBM’s Japanese consumer business division. Responsible for certifying the platform on the OpenADR 2.0 IEEE standards. Developed a suite of integration tests across international standards.
Projects
Publications
Research papers published during my stint at the RADLab, UC Berkeley. Our focus is on exploiting machine learners that get appplied everyday in common applications. For example, we attacked spam filters’ Bayesian learners with a little bit of social engineering and exploiting known vulnerabilitiy vectors. Our work culminated in a book chapter of “Misleading Learners”