Amazon advertising exchange team. Streaming analytics and auction mechanics.

Released Real-time Analytics (RTA) pipeline for the Amazon Advertising Exchange

Devised and implemented performance improvements to scale the RTA system achieving a full scale production deployment

Gained experience developing and production tuning high scale low latency solutions

Contributed improvements to enable regionally distributed processing of contextual recommendations

Developed Hive pipeline for feedback driven dynamic cache tuning and URL normalization

Technologies used include: Java, AWS (EC2, EMR, SQS, Kinesis, RDS, DynamoDB), Redis, Node JS

Bincity brings the logistics of physical collection into the 21st century. Using real-time analytics and data mining to optimize collection networks, we aim to increase the efficiency and physical security of our industry partners. Bincity's proprietary distributed sensor network gives businesses the insights they need to grow their network at a significantly increased pace, while saving time and physical resources in the process.

Developed front and backend features for a YC W12’ startup

Evaluated cost/performance metrics and implemented compute/data deployments within different cloud service providers

(Google Compute Engine, Windows Azure, Amazon Web Services EC2 and Amazon Glacier)

Evaluated workflow and infrastructure management frameworks

In consultation with an engineering client, developed a custom parser for automatic generation of parallel designs of experiment

Implemented file archival (S3/Glacier), license monitoring, 3D-Surface charts, custom middleware and data models

Improved database and algorithmic performance through ORM optimization

Hack-day project implemented stack-overflow style asset tagging (django-taggit)

Technologies used include: Python, Java, Django ORM, Postgres, Hadoop, SaltStack, Distributed Queues (Celery/SQS), JClouds, JQuery, Knockout JS, Bootstrap, D3/webgl-surface-plot, EC2, S3, Glacier, Windows Azure

Architected and developed a cloud based simulation service using Amazon Web Services

Automated startup, teardown and execution monitoring of parallel simulations within Linux clusters

Performed business needs analysis to develop solutions in a multiplatform/multilingual environment

Presented developments to international, divisional, and developer audiences

Technologies used include: Python, C#, Redis, EC2, S3, StarCluster, Oracle Grid Engine, Fluentd (centralized logging and analytics), System Security - Symmetric and Asymmetric Encryption