➢ Hybrid-Cloud Data Infrastructure: Led engineering teams to develop data platform compatibility across hybrid cloud infrastructures over public and private clouds by inserting abstraction layers for Compute & Data for application bindings➢ Data Governance: Led engineering teams that developed data policy enforcement platform to enforce data privacy and governance rules on hybrid infrastructures over multiple cloud providers➢ Data Replication: Led engineering teams that developed kafka-based horizontally scalable data replication platform across multiple cloud providers to integrate hybrid infrastructures➢ Observability: Led engineering teams developing distributed tracing platform for discovering service-to-service dependency of content supply-chain and distribution services➢ Data-Mesh Platform:Led engineering teams developing event-driven data integration platform (data-mesh) to support async integration of micro-services

➢ Data EfficiencyLed data infrastructure efficiency program for the organization and developed a framework for large scale offline data processing systems to be streamlined into a scalable, efficient & secure data lake platform.➢ Multi-Cloud Data InfrastructureLed the engineering program to develop data platform interfaces that abstracts infrastructure and enables product to scale across public & private clouds. This framework lets us develop, deploy & operate products and services across any cloud infrastructure compatible with the framework.➢ Content Supply Chain ModernizationLed the content supply-chain modernization program to help transition organization from monolithic server architecture to micro-services based event-driven architecture supported over an online data integration “DataMesh” platform.

➢ Annotation Platform: Led a team of 60+ members and developed operational processes of Facebook’s annotation platform to generate training & test data for identity matching ML workflows➢ ML Model Performance: Designed and developed metrics, data-pipelines & dashboards to measure performance of identity matching ML models which is a core feature used by all of Facebook / Meta➢ Data & AI Infrastructure: Developed collaborations between Data and AI Infrastructure teams to help support use-cases that span across both infrastructures and reduce operational overhead for the team

➢ Playback Analytics: Developed batch and streaming pipelines to ingest raw events from clients and extract playback and streaming quality metrics to support operations, analytics & A/B testing➢ Stream Processing Platform: Developed stream processing platform as a service (Jarvis) for rapid a/b experimentation and metrics calculations for iterative development of streaming algorithms➢ Online Analytics Platform: Developed custom Druid (distributed cube) modules and integrated it with Netflix’s telemetry platform (Atlas) to support rapid slice/dice analysis of client-side metrics with high cardinality at 100x lower cost with 10x better read/write query performance.

➢ Cloud Efficiency Analytics: Developed data pipelines to attribute cloud costs to applications to help drive cost efficiency initiatives at Netflix➢ Cloud Performance Analytics: Developed data pipelines on client events to identify performance bottlenecks on the Netflix product as experienced by Netflix clients (users) across the world➢ Engineering Strategy: Developed data pipelines and dashboards on code repositories and commit history to provide insights on developer efficiency, technology trends i.e. Java / javascript / NodeJS, etc. to help drive engineering strategy for future investments in tools, processes & teams

➢ Event Driven Architecture : Led development of realtime event processing platform using RabitMQ and Elasticsearch to scale GS AppStore application delivery operations by ~100x to support company’s security and software delivery functions➢ Big Data: Led infrastructure organization to adopt hadoop and distributed NoSql technologies like Elasticsearch, Spark, HBase, …

➢ Data Platform: Led development of data platform to support client relationship management, client targeting, pitch book generation, capital market deal execution for IPOs, Debt issuance, Lending transactions, etc.➢ Market Data: Developed systems that ingested market data feeds from vendors like Reuters, CapitalIQ, Moodys, Factiva, etc. and cross-referenced them at company level to produce a daily up-to-date market summary of the clients that Investment Bankers worked with on their engagements➢ Investment Banking Strategy: Partnered with banking teams on analyzing company ownership data to develop pitching strategies for mergers, acquisitions, IPO, corporate bonds, bank loans, underwriting, poison-pill or anti-raid style transactions for clients