I'm the technical lead for ETL APIs within the Databricks Lakeflow platform. Our team builds a variety of features to help customers build declarative pipelines for ingesting and transforming their data, including the industry leading AutoCDC APIs. This work includes product scoping, functionality, optimization, performance analysis, and often improvements contributed to underlying systems such as Delta Lake and Apache Spark.

I worked on the networking management plane for Google Compute Engine.

* Programmed and released Internal Load Balancing, a new feature providing proxy-free L3 load balancing fully contained within a customer's virtual network.

* Analyzed and tuned IP allocation, ensuring it performs at scale while remaining extensible for new customer requirements.

* Tweaked design of various workflows to ensure a clear and consistent state of the world at every execution step

I've also made multiple contributions to the execution engine for Spanner, Google's planet scale RDBMS. In particular, I've coded various improvements to index scan selection, and taken end-to-end ownership of implementing new utility and aggregate functions.

I worked on the query optimizer team for the Oracle RDBMS.

* In a team of 2, developed end-to-end a new approximate query processing function approx_percentile. We reviewed the literature and prototyped to determine which algorithm would perform best within our execution engine, digging through journal articles all the way up to 2013. We eventually delivered a sketch-based approach with less than half the execution time and 1/100th the memory footprint of existing percentile estimation functions.

* Improved transient plan persistence in SQL Plan Management, Oracle's framework for managing query execution plans and selecting the best one. This project added support for certain categories of plans which would previously never be considered for SQL Plan Management, allowing customers to more effectively tune performance and mitigate regressions.

* Measured performance and analyzed cost model changes for our new in-memory feature. Many queries went from severe regressions to 5x improvements after tweaking the model to properly account for the behavior of tables stored in RAM.