Gowthaman Vivekanandan

Real-Time Peer Discovery Engine
• Re-engineered the flagship peer matching system that slashed execution time from 7 minutes
to 10,000+ users
• Migrated from Elasticsearch to Qdrant vector database achieving 10x faster similarity search
across 20M+ embeddings
• Built enterprise web scraping platform processing 8M+ websites with structured data extraction
capabilities
Enterprise-Scale AI Infrastructure
• Architected, built, and deployed a distributed multi-cloud pipeline (Azure + GCP) with 80
concurrent batch jobs across 10 regions
• Processed 120B tokens in 2 hours using Gemini Flash 2.5 on Vertex AI - a scale previously
impractical, unlocking new business opportunities such as semantic enrichment of millions of
companies, real-time peer discovery, supplier/ownership mapping, product tagging and much
more
Data Lakehouse Architecture
• Built ACID-compliant data lakehouse using Spark, Delta Lake, Hive, and Azure Blob Storage
on Kubernetes
• Implemented medallion architecture (bronze -> silver -> gold -> platinum -> diamond) for datalake
• Automated schema onboarding and data quality validation reducing debugging effort by 60%
and trusted reports for customers
API Performance Engineering
• Reduced API response latency from 6s to replacing it with targeted, efficient data access patterns
• Streamlined authentication by shifting from DB lookups to lightweight token-based validation,
cutting unnecessary database load
• Consolidated nested queries and applied selective caching, removing redundant data fetches
• Achieved these improvements in just 2 weeks, surpassing a year of prior optimization efforts
and delivering a step-change in user experience

• Unified SEO Data Lake: Created centralized data platform by migrating legacy Java microservices to Onehouse + Apache Hudi, consolidating billions of keyword rankings, backlinks, and content signals into a single queryable lake. Reduced analysis time from hours to seconds, enabling real-time competitor and content performance insights.
• Analytics Performance Optimization: Orchestrated migration from AWS Redshift to StarRocks, cutting customer-facing dashboard query latency from 20s to
• Data Governance & Reliability: Established Glue Catalog + Confluent Schema Registry for centralized schema management, ensuring integrity and preventing silent data corruption across rapidly evolving SEO datasets.
• Reengineered existing ETL workflows to improve performance by identifying bottlenecks and optimizing code accordingly.
• Ensured data quality through rigorous testing, validation, and monitoring of all data assets, minimizing inaccuracies and inconsistencies.

• Designed multi-domain data warehouse integrating manufacturing, telematics, and customer data on Hadoop/Spark/Hive
• Implemented GDPR-compliant data governance using Apache Ranger for secure cross-team data sharing
• Developed no-code data ingestion platform reducing onboarding time from weeks to days
• Created automated validation pipelines reducing downstream data incidents by 95%

• Architected multi-tenant analytics platform from MVP to enterprise production deployment
• Implemented row-level security and role-based access control for strict tenant data isolation
• Integrated enterprise SSO (GSuite, Active Directory) supporting large-scale enterprise adoption
• Engineered PostgreSQL at scale with partitioning, indexing, and high availability strategies
• Led engineering team in architecture decisions, task planning, and code reviews