Venkata Revunuru
Senior Bigdata/Elasticsearch Engineer
Venkata Revunuru serves as Senior Search Engineer and Enterprise AI Architect with over 12 years of experience designing, scaling, and optimizing enterprise search and data platforms across distributed cloud environments. Based in the Greater Phoenix Area, he specializes in Elasticsearch-based architectures, distributed ingestion pipelines, search relevance optimization, and AI-assisted retrieval systems for large-scale enterprise applications.
Currently at Carvana since November 2019, Revunuru leads the end-to-end implementation of Elastic Stack for centralized logging and observability, integrating telemetry from applications, infrastructure, network, and security into unified platforms serving SRE, security, and product teams. He has architected and managed Elastic Cloud clusters on Azure supporting billions of documents with high-throughput search and analytics workloads. His implementation of Index Lifecycle Management with hot-warm-cold tiering and advanced shard optimization strategies achieved a 40% reduction in query latency while delivering over $500,000 in annual infrastructure cost savings. He successfully led a cluster split architecture separating search and vehicle workloads, resulting in a 30% performance improvement by eliminating noisy-neighbor conflicts. His work encompasses building real-time data pipelines using Kafka and Databricks for near-instant ingestion and analytics across multiple business units, establishing comprehensive observability frameworks with Kibana and Datadog dashboards for 24/7 system reliability, and implementing hybrid search capabilities combining BM25 and vector similarity for enhanced natural-language relevance.
Prior to Carvana, Revunuru worked as Senior Big Data/Elasticsearch Engineer at United Airlines from November 2018 to November 2019, where he managed Kafka and Elasticsearch clusters for large-scale ingestion pipelines supporting global airline operations. He reduced query latency by 35% through advanced index tuning and analyzer optimization while delivering zero-downtime Elastic migrations and scaling strategies. His development of Spark/Scala pipelines achieved 40% higher throughput, complemented by real-time Kibana dashboards for operational monitoring.
From September 2017 to November 2018, he served as Big Data Engineer at 84.51°, Kroger's analytics subsidiary, where he architected real-time ingestion pipelines using Kafka and Spark for instant retail transaction processing and analytics. He designed cost-efficient data lake strategies with AWS S3 and Hive while improving data pipeline reliability and performance by 40% through Spark/Scala optimizations. His work included implementing Delta Lake optimization policies and developing replay frameworks for backfills without impacting live service level agreements.
Currently at Carvana since November 2019, Revunuru leads the end-to-end implementation of Elastic Stack for centralized logging and observability, integrating telemetry from applications, infrastructure, network, and security into unified platforms serving SRE, security, and product teams. He has architected and managed Elastic Cloud clusters on Azure supporting billions of documents with high-throughput search and analytics workloads. His implementation of Index Lifecycle Management with hot-warm-cold tiering and advanced shard optimization strategies achieved a 40% reduction in query latency while delivering over $500,000 in annual infrastructure cost savings. He successfully led a cluster split architecture separating search and vehicle workloads, resulting in a 30% performance improvement by eliminating noisy-neighbor conflicts. His work encompasses building real-time data pipelines using Kafka and Databricks for near-instant ingestion and analytics across multiple business units, establishing comprehensive observability frameworks with Kibana and Datadog dashboards for 24/7 system reliability, and implementing hybrid search capabilities combining BM25 and vector similarity for enhanced natural-language relevance.
Prior to Carvana, Revunuru worked as Senior Big Data/Elasticsearch Engineer at United Airlines from November 2018 to November 2019, where he managed Kafka and Elasticsearch clusters for large-scale ingestion pipelines supporting global airline operations. He reduced query latency by 35% through advanced index tuning and analyzer optimization while delivering zero-downtime Elastic migrations and scaling strategies. His development of Spark/Scala pipelines achieved 40% higher throughput, complemented by real-time Kibana dashboards for operational monitoring.
From September 2017 to November 2018, he served as Big Data Engineer at 84.51°, Kroger's analytics subsidiary, where he architected real-time ingestion pipelines using Kafka and Spark for instant retail transaction processing and analytics. He designed cost-efficient data lake strategies with AWS S3 and Hive while improving data pipeline reliability and performance by 40% through Spark/Scala optimizations. His work included implementing Delta Lake optimization policies and developing replay frameworks for backfills without impacting live service level agreements.
Earlier in his career, from May 2012 to December 2015, Revunuru worked as Database Developer at LiquidHub in Bengaluru, India, where he developed and optimized SQL stored procedures, triggers, and ETL pipelines for enterprise-scale transactional systems. His query tuning, indexing, and partitioning strategies improved reporting performance by over 50%, while his automated purge and archival processes ensured compliance and storage efficiency for high-volume transactional datasets.
Revunuru also founded Snaplocal, where he served as CTO for an applied industry project, designing and implementing the search and discovery layer for a hyperlocal community platform. He built location-aware indexing, relevance tuning, and content discovery workflows while applying enterprise-grade search architecture principles in a real-world consumer application context.
His professional accomplishments include extensive hands-on ownership of production-grade search platforms with strict reliability and performance requirements, experience mentoring engineers and defining best practices for search, ingestion, and observability systems, and applying enterprise search architecture principles across both large organizations and independent industry projects.
Revunuru holds a Master's degree in Computer and Information Systems Security/Information Assurance from Wilmington University, completed between 2015 and 2017 with a 3.8 GPA. He maintains active certifications as a Cloudera Certified Spark & Hadoop Developer and IBM Certified Developer for Apache Spark, and holds a USPA A License for skydiving.
His technical expertise encompasses advanced proficiency in Elasticsearch, Query DSL, Kibana, search relevance engineering, distributed search architecture, Kafka, Spark, Databricks, Azure, AWS, Python, SQL, AI-assisted retrieval systems, vector search, hybrid retrieval concepts, machine learning, NoSQL databases, Linux administration, data governance, ETL pipelines, Terraform, and cloud architecture.
Revunuru also founded Snaplocal, where he served as CTO for an applied industry project, designing and implementing the search and discovery layer for a hyperlocal community platform. He built location-aware indexing, relevance tuning, and content discovery workflows while applying enterprise-grade search architecture principles in a real-world consumer application context.
His professional accomplishments include extensive hands-on ownership of production-grade search platforms with strict reliability and performance requirements, experience mentoring engineers and defining best practices for search, ingestion, and observability systems, and applying enterprise search architecture principles across both large organizations and independent industry projects.
Revunuru holds a Master's degree in Computer and Information Systems Security/Information Assurance from Wilmington University, completed between 2015 and 2017 with a 3.8 GPA. He maintains active certifications as a Cloudera Certified Spark & Hadoop Developer and IBM Certified Developer for Apache Spark, and holds a USPA A License for skydiving.
His technical expertise encompasses advanced proficiency in Elasticsearch, Query DSL, Kibana, search relevance engineering, distributed search architecture, Kafka, Spark, Databricks, Azure, AWS, Python, SQL, AI-assisted retrieval systems, vector search, hybrid retrieval concepts, machine learning, NoSQL databases, Linux administration, data governance, ETL pipelines, Terraform, and cloud architecture.