Led the redesigning, refactoring, QA-ing, and shipping of a side binary with the Datadog Agent to simplify the user experience for pulling AWS, Azure, etc. backend secrets.

Developing a replacement to the Datadog Agent’s existing configuration management layer, to optimize performance with regards to speed, space constraints, reliability, and security.

Develop and maintain key integrations to the Datadog Agent, including vSphere, Openstack, Cloudera, and Jenkins, using Python, Go, and Java with a strong emphasis on Test-Driven Development.

Deploy and manage test environments on AWS and GCP to support integration testing and development.

Worked cross-functionally with engineers, designers, etc., to cover feature and design gaps complicating the transition of cloud observability platforms of large enterprises from Microsoft Azure to Datadog.

Launched a credential expiration and permissions tracking feature for partnered Azure Key Vaults.

Investigated performance isolation challenges in a project involving the disaggregation of Local SSDs in Google servers into rack-shared SSD trays. This involved using fio (flexible I/O) scripts in a Linux environment to represent internal customer workloads, and identify gaps in relevant scenarios of noisy neighbor workloads.

Strengthened the tracing, playback, and trace visualization infrastructure of shared storage devices, by developing and maintaining tools in Python to help analyze and understand their performance.

Designed and deployed end-to-end data-plane probes (mostly in the Java 17 runtime) in the Google Cloud Functions framework that will be used to evaluate the maintenance of serverless infrastructure.

Uncovered bugs regarding missing coverage in prober validation logic; documented, implemented, and pushed fixes to said bugs that existed in the system for 2+ years.

Wrote and tested algorithms in Go that analyze the performance of various cloud infrastructure tools.