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Apache Spark is the largest open source project in data processing with a state of art execution engine built around speed, ease of use, and sophisticated analytics.Github: https://github.com/apache/spark

Area Tech Lead of Large-Scale Data Analytics at Facebook. I work with a team of 50+ amazing engineers in building distributed systems and databases that scale across geo-distributed clusters of hundreds of thousands of machines.Received additional/discretionary equity (typically reserved for the top 1% of employees at Facebook) for all five consecutive years during my tenure.

Joined as one of the first 10 engineers and led the open source Apache Spark team (as a TL and an EM) with a deep focus on performance, scalability and security. Key contributions include:1. Project Tungsten [1], SQL-Based Access Control [2] , Cost-Based Query Optimizer [3], Approximate Queries [4], and several key query optimizations and features in Apache Spark across SQL, PySpark and Spark Core.2. Created Databricks Vault, Databricks' first automatic deployment engine that continuously and securely deployed our services for several hundred customers on several thousand machines.3. Created Databricks' Pay-As-You-Go pricing infrastructure that continuously synced, reported and charged customers based on their usage while operating at the scale of several thousand machines and millions of dollars.[1] https://spark-summit.org/eu-2016/events/sparks-performance-the-past-present-and-future[2] https://docs.databricks.com/spark/latest/spark-sql/structured-data-access-controls.html[3] https://spark-summit.org/2017/events/cost-based-optimizer-in-apache-spark-22/[4] https://spark-summit.org/2015/events/blinkdb-ola-supporting-continuous-answers-in-sparksql

Thesis: BlinkDB: Queries with Bounded Errors & Bounded Response Times on Very Large DataAdvisor: Ion Stoica