Oversee the engineering function which designs, develops, and deploys scalable technologies across Robinhood. Directly responsible for Data Science, QA/QE, TPM, Infrastructure, Activations, Platform and Payments teams. Work alongside Robinhood's General Managers and Product Engineering leaders for Robinhood's three business lines: Brokerage, Crypto, and Money.

Head of Infrastructure Engineering

I lead the Brokerage Engineering team at Robinhood which consists of the following teams:* Stock Trading* Options Trading* MarketData Systems* Order Management and Routing Systems* Recurring Investments* Clearing and Settlement

In my second year at FarmLogs, I matured as a leader and became responsible for hiring and managing the backend team at FarmLogs. In the past one year, I have:- Hired and retained a team of 5 full-time senior backend engineers, and mentored 2 junior interns.- Architected, led and implemented the backend implementation for high-precision satellite imagery project. Built a robust and efficient pipeline to download and process tens of terabytes of high-precision satellite data. Using sophisticated machine-learning algorithms, this imagery pipeline is able to detect problems in user’s farms and able to alert growers in near real-time. This project was a huge success and played a key role in helping FarmLogs secure its Series-B funding of $10 million.- Managed backend implementation of several revenue generating projects: next version of rainfall events, billing infrastructure, nitrogen monitoring and recommendations.

I was a very early employee at FarmLogs and only the second backend engineer besides the CTO. During my first year, I:- Helped mature the overall engineering process at FarmLogs.- Automated the testing infrastructure and the build system.- Setup pageryduty for the production services to improve uptime of the app.- Cut down the latency of the backend clojure app by more than 50%. This resulted in better user-experience and decreased infrastructure costs.- Saved the company thousands of dollars per month in infrastructure costs by migrating databases from Heroku to AWS.- Was a key voice in making several important initial hires.- Architected and implemented backend APIs for several important features using Clojure, Python, RabbitMQ, and Postgres.- Automated ingest of several public datasets as well as implemented APIs for them.

I started at the Climate Corporation when it was still called Weatherbill and had less than 20 engineers. For more than two years, I have worked on several key projects across various teams in the company, leading up to its acquisition.I lead engineering effort across multiple teams to deliver many internal services that drive the Total Weather Insurance (TWI) policies at www.climate.com. Part of my work involves implementing large scale map/reduce jobs using Cascalog and Clojure, and running them on Amazon Elastic Map Reduce. I have many months of experience in debugging/optimizing hadoop jobs.I work closely with researchers in Statistics and Machine Learning to build models to forecast the statistical distribution of crop yields, and understand the relationship between weather and crop yields.

• Apply statistical techniques to predict the clickthrough/conversion rate for advertisement cam-paigns on the internet.• Make the existing MATLAB codes run faster by using Parallel Programming toolbox, or rewritingthe functions in C.• Debug and detect erroneus campaign behavior to minimize revenue loss in our network.

• Assisted Prof. Sanjay Lall in the courses Linear Dynamical Systems (EE 263), and Linear ControlSystems II (ENGR 207b)• Designed exam questions, took review sessions and office hours with over 60 students.

Applied convex optimization to develop optimal or near-optimal policies for constrained controlproblems with linear stochastic dynamical systems.Publications:Mayank Agarwal, E. Cinquemani, D. Chatterjee, and John Lygeros, "Convex relaxations of stochastic optimization programs with chance constraints", in Proceedings of the European Control Conference, 2009, pp. 2827–2832.E. Cinquemani, Mayank Agarwal, D. Chatterjee, and J. Lygeros, "Convexity and convex approximations of discrete-time stochastic control problems with constraints". Automatica, Vol. 47, No. 9, pp. 2082-2087, 2011.

• Developed an algorithm to enable failure prognostic assessment of electronic components.• Coded the algorithm in Visual C++ to deliver final installable software named PHM Toolkit. This software is currently being used by many PhD students at CALCE for their research.