Vishwak Thatikonda
Starbucks
Dublin, California, United States
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Vishwak Thatikonda is a Software Engineer with over 11+ years of experience developing cutting-edge engineering solutions across full-stack development, REST APIs, cloud services, and large-scale web applications. He is skilled in JavaScript, TypeScript, Python, Node.js, React, GraphQL, MongoDB, PostgreSQL, and AWS, with experience owning, architecting, building, and maintaining major product and infrastructure components.
He is currently a Full Stack Developer at Starbucks, where he serves as the Lead UI Engineer for the Product Data Hub, supporting Starbucks’ next-generation POS system, global inventory architecture, and product data workflows across 38,000+ global stores. His work includes engineering master data management platforms, store assortment systems, dynamic filtering engines, contact-center applications, AWS Lambda backends, reporting pipelines, and analytics dashboards.
Previously, Vishwak worked as a Lead Full Stack Developer at Kyte, where he directed a global team and contributed to systems that supported a $1.1M revenue increase, a 13x increase in organic traffic, and an 85% reduction in partner integration timelines. He also served as a Senior Full-Stack Developer at Credit Sesame, leading technical work for banking products, credit-building tools, rent-based credit reporting, and multi-tenant platform capabilities serving financial institutions and millions of members.
Earlier in his career, Vishwak was a Full-Stack Developer at AirWorks Solutions, where he was the founding engineer and helped build the GeoAI SaaS platform and AWS infrastructure from the ground up, and a Full-Stack Developer at Augmate Corp., where he worked on wearable device management, blockchain-based asset tracking, and enterprise IoT systems. He also contributed to NASA Perseverance rover Mastcam-Z mission software while working at Arizona State University.
Vishwak holds a Master’s in Computer Science from Arizona State University and a Bachelor’s in Computer Science from Amrita University. He is also an AWS Certified Solutions Architect Associate.
Area of Expertise
Topics
From Analytics to Action: Re-Architecting Master Data Management for Agentic Enterprise AI
Enterprise software is rapidly evolving from isolated text-generation tools to autonomous agents capable of executing complex operational workflows, from real-time inventory placement to corporate rewards orchestration. As AI systems move from recommendation to execution, the quality, consistency, and timeliness of enterprise data become mission-critical. Traditional analytics-grade data architectures, however, were not designed to support autonomous decision-making at scale. In agentic environments, near-matches, stale records, duplicate entities, or data drift can trigger significant downstream errors when decisions are executed directly across enterprise systems.
This session explores the architectural shift required to evolve Master Data Management from legacy, batch-processed data tables into an action-grade, immutable contract layer built for agentic enterprise AI. It explains why MDM must move beyond its traditional role as a reporting and analytics foundation and become an operational execution layer that enables reliable, real-time decisioning.
Drawing on experience building enterprise master data hubs, including a global system orchestration framework spanning 38,000 retail locations, the presentation will outline the system-strategy decisions required to create a stable data substrate for autonomous workflows. Attendees will learn practical approaches for implementing sub-second match-and-survive functions, real-time routing, idempotent workflows, versioned data contracts, event-driven fan-out patterns, and structural methods for reducing data drift using technologies such as AWS Lambda, DynamoDB, and EventBridge.
Designed for enterprise architects, data leaders, AI platform teams, and technology decision-makers, this session provides a practical framework for re-architecting MDM for action-oriented AI environments, where identity resolution, data quality, and real-time routing directly influence operational outcomes.
From Analytics to Action: Re-Architecting Master Data Management for Agentic Enterprise AI
Enterprise AI is rapidly evolving from isolated text-generation tools to autonomous agents capable of executing complex operational decisions across business-critical workflows. These agents are beginning to support functions such as real-time inventory placement, system orchestration, and corporate rewards management. Yet most enterprise data architectures were designed for analytics, reporting, and retrospective insight—not for autonomous decision-making where actions are executed directly against systems of record.
This session examines why Master Data Management must evolve from a legacy, batch-oriented data foundation into an action-grade operational layer for agentic enterprise AI. In autonomous environments, stale records, near-match identity errors, duplicate entities, and data drift can create serious downstream consequences when AI-driven decisions trigger live enterprise workflows. Reliable agentic systems therefore require a new MDM architecture built around immutability, versioned data contracts, real-time identity resolution, and event-driven execution.
Drawing on experience building enterprise master data hubs, including a global system orchestration framework spanning 38,000 retail locations, the presentation will explore the architectural and system-strategy decisions required to create a stable data substrate for AI-enabled operations. Attendees will learn practical approaches for implementing sub-second match-and-survive capabilities, real-time routing, idempotent workflows, and event-driven fan-out patterns using technologies such as AWS Lambda, DynamoDB, and EventBridge.
The session will also address structural methods for reducing data drift and maintaining trusted entity resolution as autonomous agents interact with critical enterprise systems. Designed for enterprise architects, data leaders, AI platform teams, and technology decision-makers, this presentation offers a practical roadmap for re-architecting MDM for action-oriented AI environments where data quality directly determines operational outcomes.
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