Gopinath Ramisetty

Gopinath Ramisetty is a big data and distributed systems engineer whose 16-year career has been shaped by one recurring challenge: turning massive datasets and complex business rules into platforms that run fast, reliably, and at enterprise scale. Working across global financial services, telecommunications, and enterprise technology, he has consistently operated where engineering decisions have direct commercial consequences—reducing processing windows from hours to minutes, enabling real-time personalization, and modernizing legacy systems into cloud-native architectures that can scale predictably.

At American Express, where he served as Senior Software Engineer II, Ramisetty’s work centered on modernizing high-volume targeting and eligibility systems that influence customer experiences and merchant outcomes across a global cardholder base. He led modernization of legacy systems into cloud-native microservices using Spring Boot and GraphQL APIs, supporting services used by over 140 million cardholders across 25 countries. His technical toolkit spans Apache Spark and Hadoop ecosystems, along with Google Cloud services such as BigQuery, Composer, and Spanner—allowing him to operate across both batch and real-time architectures. He also brings deep experience across NoSQL and search platforms (BigTable, HBase, Cassandra, Elasticsearch) and streaming systems (Kafka, MapR Streams), enabling end-to-end platform delivery from ingestion to decisioning and API distribution.

One of his defining contributions at Amex was architecting the mTarget platform for Amex Offers—a dynamic, graph-based execution engine built on BigQuery and Spark that transformed targeting workflows. The measurable outcome was a dramatic reduction in processing time: from 6–8 hours down to ~20 minutes, while handling 2–3 petabytes of data daily—a roughly 20× performance improvement. Through the STEM initiative, he developed an Enterprise Eligibility Platform that automated eligibility calculations at global scale, processing 2,000+ offers daily and enabling eligibility computation across Amex’s international cardholder base. His modernization work also shows an evolution of performance engineering: a Big Data Eligibility Engine initially built on MapReduce and Hive reduced processing from weeks to 24 hours, and later modernization to Spark/Scala further reduced runtimes into the single-digit-hour range.

Ramisetty’s work also extended into real-time systems and high-throughput APIs. He developed the Realtime MYCA Offers Eligibility API, enabling personalized offers via the American Express web and MYCA app. To meet scale and latency requirements, he engineered a multi-threaded API integration framework capable of 600+ transactions per second with approximately 600ms response time while integrating 10+ external APIs. He also created a dynamic Java rule engine that reduced memory usage by 8× while delivering eligibility computations in around 20ms—a concrete example of how algorithmic design and systems constraints can be aligned for real-time decisioning.

In telecommunications, at Synchronoss Technologies, Ramisetty contributed to an Advanced Analytics platform using Apache Spark to process multi-terabyte datasets daily, supporting analytics for 11 million subscribers. He implemented Spark MLlib workflows to produce predictive insights and customer analytics—an extension of his data engineering strengths into production machine learning pipelines.

Earlier roles across Impetus Technologies, Dex One Services, Zen3 Infosolutions, Infosys, HSBC, Amdocs, and Alacriti show breadth and consistency: building batch and streaming pipelines (Hadoop, Spark, MapReduce, Hive, Kafka, HBase), engineering low-latency APIs, and delivering real-time systems for major clients including American Express, Apple, BASF, HSBC, and Rogers Wireless. Across this arc, he also invested in mentorship and knowledge transfer—training teams on Spark optimization, big data systems, and cloud practices, and collaborating with data scientists to operationalize analytics under data quality and monitoring discipline.

A consistent theme in his work is responsible enterprise engineering. He has implemented security and compliance mechanisms such as HashiCorp Vault, JWT, IDaaS, encryption controls, and Spring Security, pairing innovation with governance and privacy requirements. In sum, Gopinath Ramisetty’s profile reflects a sustained record of engineering leadership in platforms that combine petabyte-scale processing, real-time decisioning, and cloud-native modernization—delivering measurable performance, reliability, and business value.