In my 15 months at Uber, I led the two most impactful projects in the marketplace mobility organization.

One project was purely in matching mechanics (algorithms) and led to a global 1% increase in Gross Bookings.

The other project is publicly known as Trip Radar. I led the three verticals (Engineering, Product and Data Science) across 3 organizations (marketplace, pricing and driver experience) to land this project safely globally in many products. This project increased the reliability of all TripRadar products significantly (different products behaved differently but on average it increased reliability by 5%) while giving more options to drivers.

The idea on this project was to allow drivers to see all jobs around them instead of giving them only one option. This required a fundamental change in all Uber infrastructure since it allows a many to many matching between jobs and drivers.

Started a new way of thinking about marketplace problems by building an OLAP infrastructure (Marketplace Metrics and Signals Store) that ingests all matching, rider and driver actions on the platform and allows the system to dynamically decide what mechanisms to activate in the system for increasing reliability and revenue.

During my three year tenure at Google I went from a Junior engineer to one of the most influential engineers in our org. I acted as:

• The Sole Lead Backend Engineer for any Gsuite related project. I worked closely with engineers from Gmail, Google Chat, Drive, Calendar, etc on gathering requirements, designing and implementing common infrastructure

• Tech-lead of the ingestion team at People stack

• Co-represented over 40 engineers working with Site Reliability Engineers

• Privacy point of contact for our team

I have finished many end to end projects at Google. However, here I will only list four projects that I led/co-led that won a company wide award:

• Bronze performance award on Memory utilization: The first project I did at Google was rewriting a GO map-reduce pipeline in C++. While translating, I found many algorithmic inefficiencies. The new algorithm saved hundreds of terabytes of RAM on every run (twice a day).

• Silver latency optimization: One of my projects was launching a recommendation system in the new Google chat project. While launching the project even though I was not in the Chat organization I found inefficiencies in their logic and calls to spanner. The optimization led to 95% latency optimization at 99 percentile.

• Silver CPU optimization: I worked on decommissioning a complex legacy system (95K Lines of Code) and after moving to the new system the analysis showed more than 40% CPU optimization.

• Platinum Code health award: After seeing the state of our code base and attending org-wide architecture summits led by our strong SREs I *solely* found many snow-flakes (adhoc code paths due to legacy or bad implementation) in our system and took action to remove all of them. These flaws are all related to code health that makes the system complicated, hard to understand and debug. In addition, new features sometimes had to be implemented more than once. This project was called snow-plow by leads in the team since it removed all snowflakes.