Parikshit Agarwal

I architected a cloud-native Risk Data Hub for a leading global bank, enabling the governed ingestion, transformation, and consumption of over 300 TB of enterprise data. The platform consolidated both assured and unassured data sources into a scalable analytics ecosystem that supports risk management and regulatory reporting.

I led the design of enterprise data models, Data Mesh governance, RBAC, metadata management, and auditability frameworks. The solution leveraged event-driven ingestion, standardized transformation patterns, and self-service data products, significantly improving data discoverability, ownership, and operational efficiency. The platform established a trusted foundation for enterprise analytics and future AI initiatives.

I led the design of an enterprise-wide data architecture initiative for a global manufacturing organization operating more than 200 ERP systems across multiple plants and mills. I developed a canonical enterprise data model that unified disparate operational systems into a governed and scalable data platform.

This project also involved establishing data governance processes covering ownership, stewardship, lineage, cataloging, security, and data quality. I designed the architecture for a cloud-native data lake and analytics platform that enabled consistent reporting, improved data trust, and laid the foundation for advanced analytics and AI-driven decision-making.

I designed and implemented a metadata-driven orchestration framework to automate large-scale data pipeline execution and governance. The platform supports event-based, schedule-based, and dependency-driven workflows through centralized metadata configuration rather than hard-coded orchestration logic.

Key capabilities include auditability, lineage tracking, SLA monitoring, data quality validation, automated notifications, and scalable execution across thousands of pipelines. The framework reduced operational complexity, improved maintainability, and accelerated onboarding of new data products while ensuring governance and compliance standards were consistently enforced.

I developed an automation framework to accelerate the migration from Informatica PowerCenter to Oracle Data Integrator (ODI). The solution analyzes Informatica metadata, mappings, workflows, and transformations, then automatically generates ODI-compatible artifacts and configurations.

The framework significantly reduces manual effort, migration risks, and project timelines while ensuring consistency across large migration programs. Built using Java and Python, the solution incorporates metadata extraction, rule-based conversion, validation mechanisms, and automated documentation generation.

I created an automated framework for synchronizing bulk and incremental data from relational databases into graph databases. The solution eliminates the complexity of maintaining graph structures by automatically detecting and propagating data changes while preserving relationships and dependencies.

Designed for high-volume environments, the framework supports near real-time synchronization and enables organizations to leverage graph analytics without redesigning existing operational systems. The project demonstrates expertise in data integration, graph databases, automation, and scalable architecture design.

I architected an enterprise analytics platform for IT asset management, integrating data from multiple operational systems into both relational and graph-based data stores. The platform provided end-to-end visibility into asset relationships, dependencies, ownership, and lifecycle management.

Leveraging graph analytics and enterprise reporting, the solution enabled improved decision-making, impact analysis, and operational efficiency. The architecture incorporated governance controls, data quality processes, and scalable reporting capabilities supporting a large telecommunications organization.