Shivakumar Shivampeta

AI-Augmented Customer Data Platforms: Engineering for Scale, Speed, and Complianc

Abstract:

This presentation examines a cutting-edge Customer Data Platform architecture designed to process billions of daily events across AWS and GCP cloud environments. The system leverages Apache Kafka-based event-driven processing to enable real-time identity resolution and personalization while maintaining stringent privacy compliance standards. Key architectural innovations include infrastructure-as-code provisioning through Terraform, sophisticated machine learning integration for dynamic segmentation, and predictive workload management for Apache Spark operations.
The multi-cloud implementation achieves active-active synchronization across geographic regions, delivering exceptional reliability while satisfying data sovereignty requirements. The identity resolution framework employs both deterministic and probabilistic matching techniques, achieving up to 60% improvement in cross-channel attribution accuracy. Privacy-by-design principles permeate the architecture through attribute-level consent management, dynamic data masking, and comprehensive audit logging capabilities.
Machine learning integration transforms static customer segments into dynamic, behavior-driven classifications using hybrid supervised and unsupervised approaches. The predictive workload management system analyzes historical Spark job metrics to forecast computational requirements, reducing infrastructure costs by nearly 25% while maintaining performance agreements. Production deployments demonstrate remarkable technical performance, processing over five billion daily events with minimal latency for real-time personalization.
Business impact metrics validate the architectural approach, with organizations reporting substantial improvements in conversion rates and exceptional returns on investment. The governance framework addresses complex regulatory requirements through policy-based controls, automated retention schedules, and granular access management. This comprehensive approach provides a blueprint for next-generation CDP implementations, balancing the demands of scale, speed, and compliance in modern marketing technology landscapes while establishing foundations for sustainable competitive advantage through enhanced customer understanding and engagement capabilities.

Profile:

Shivakumar Shivampeta is an accomplished Senior Software Engineer with over 16 years of experience architecting enterprise-scale data solutions across industries including marketing technology, telecommunications, retail, and financial services. Currently at Epsilon (Publicis Groupe), he leads the development of advanced Customer Data Platform capabilities, designing systems that process billions of daily events while maintaining strict privacy compliance.
His expertise spans the entire data engineering ecosystem, from real-time streaming architectures using Kafka and Spark to cloud-native platforms on AWS, Azure, and GCP. Shivakumar has consistently delivered transformative results, including reducing ETL runtimes by 90%, improving query performance by 85%, and architecting identity resolution systems processing 500M+ records daily.
Throughout his career at companies like Acxiom, AT&T, Verizon, and PepsiCo, Shivakumar has pioneered innovative solutions ranging from AI-driven customer insights to enterprise data lakes. His technical proficiency encompasses modern data processing frameworks (Spark, Flink, Beam), programming languages (Scala, Python, Java), and cloud platforms (Databricks, Snowflake, BigQuery). He's particularly skilled in building ML-ready data pipelines and implementing feature engineering at scale.
Beyond technical implementation, Shivakumar excels as a strategic partner who aligns architecture decisions with business objectives. He's led cross-functional teams, mentored engineers, and established best practices across organizations. His work has directly enabled personalized marketing campaigns, real-time customer analytics, and data-driven decision making for Fortune 500 companies.
With a Master's in Computer Applications from Osmania University, Shivakumar combines deep technical knowledge with business acumen to deliver scalable, secure, and innovative data solutions. His passion for solving complex data challenges and enabling AI-driven insights continues to drive measurable impact across enterprise initiatives.