Binu John

Lead the Runtime Performance and Observability Engineering teams. *** Looking for anyone with a passion for improving performance of large scale systems to join the Performance Engineering team (fully remote)*** *** Looking for software engineers with experience in building Observability solutions and/or large scale distributed systems to join the Observability Engineering team (SF Bay Area)*** The charter of the Performance Engineering team is to measure, analyze, and improve the performance of the various products and components within Databricks and deliver the best performing data processing solution in the market. The team consists of world class specialists who deep dive and fix performance issues - large scale distributed systems to query processing to low level hardware optimizations. The Runtime Observability Engineering team is responsible for building the next generation monitoring and observability capabilities into Spark and other components that power the various products within Databricks.

Capacity Engineering team is responsible for the projection and allocation of compute and storage resources to all services at Facebook. The work entails a mix of building capacity demand model, growth forecasting (data science heavy) and strategic long term planning of capacity supply. The role is cross functional and involves interacting with the leaders of all major services within Facebook. Facebook prides itself in running a very efficient infrastructure by optimal hardware designs and efficient software systems. As the Services performance team, we run the efficiency programs that sets standards, tracks progress and work closely with service owners to optimize the software stack and meet the goals. The performance work covers a broad spectrum from hardware level code optimizations to multi region load balancing to optimization of flash and HDD based storage systems. Work closely with the hardware team to design the next generation systems that provides drastic reductions in total cost of ownership (both in terms of dollars and power). The intersection of hardware and software is an exciting place to be where I am forced to learn the intricacies of largest services at Facebook and provide guidance on hardware system design.

Engineering Manager in Performance and Capacity Engineering Manage capacity and drive efficiency across large product groups and storage services. Work with HW Engineering to define the specification of next generation H/W systems and provide insight to data center planning for global deployment of capacity. This role has deepened by performance analysis skills, hands on experience into how to build and deploy extremely large scale systems, a detailed understanding of software architectures of exabyte scale distributed storage systems (data warehouse, photo/video storage, HBase), highly distributed and low latency services (Feed, Search, Ads, Instagram, Messaging), the largest social graph in the world and in-depth knowledge of tuning and deploying flash based services.

Take on different engineering roles to provide a stable, scalable, and high performing platform to service upwards of hundred million page views per month. Infrastructure Engineer: Design, implement and release various backend infrastructure pieces to enhance the scalability, reliability, and reduce latency of the Ning platform. Projects include dynamic discovery mechanism for web apps to be visible to each other, enhancement of content store services that persist large amounts of data to RDBMS, various proxy services for performance and search engine optimizations. Performance Engineer: Responsible for all things related to performance at Ning. In the initial days, the focus was on improving the scalability, and throughput of the back-end systems. Improved the overall performance of the platform by identifying performance bottlenecks through performance analysis and later implementing solutions to eliminate the problems. Solutions touched all parts of the platform, presentation tier (PHP), application tier (Java), and databases (MySQL). In the last year, the primary focus has been on improving the page load time at the client. Several optimization techniques have been introduced to reduce the page load time. Page load time analysis was carried out by running a private instance of Webpagetest on EC2 and by using a variety of browser profiling tools. Data Engineer: Work with large data sets to create a variety of dashboards, including the performance dashboard that tracks the page delivery and page load time statistics as well as the spam dashboard that tracks the number of documents that have been classified as spam. Used a variety of technologies including Hadoop, Pig, Netezza, and Tableau. Co-author (along with Charlie Hunt) of Java Performance book which is avalaible on Amazon at http://www.amazon.com/Java-Performance-Charlie-Hunt/dp/0137142528 and online at http://my.safaribooksonline.com/book/programming/java/9780137001040.

Performance lead for Web 2.0, SOA, and Java EE technologies. Provide guidance to product teams in a variety of ways - set performance requirements for different releases, identify and fix performance issues, develop benchmarks (internal and industry standard ones through participation in SPEC), deliver best practices for the use of Sun technologies.

Manage a team of engineers focused on improving the performance of Java SE, Java EE, Sun Java System Application Server, Java APIs for Web Services and XML on multiple operating systems (Solaris, Windows, Linux) and hardware platforms.

Technical account manager of independent software vendors (ISVs) in content management, web services, streaming media and wireless market segments. Assist Sun partners in adopting, supporting, releasing and sustaining Sun products and technologies in a timely and cost efficient manner. Influenced key decision makers within ISV organizations to develop product roadmaps and strategies that aligned with Sun's vision.

Member of an engineering team that developed software for medical image archival and image visualization. Developed the patient data browser for the CORBA based medical image archival system. The image visualization package supported several two dimensional image processing functionalities as well as surface and volume rendering that allowed radiologists to create three dimensional views from two dimensional CT and MR images.

One of the six finalists for ASME national student paper competition in 1992. One of the six finalists for ASME national student paper competition in 1993.