• Led the Data and Platform Transformation for an international fast-food corporation, managing data streams from 35,000 restaurants across 60 countries. This involved developing on AWS with multiple data strategies, focusing on areas like Automation, Infrastructure, Processing, Ingestion, & storage. Revamped crucial sales processing & and reporting systems with serverless, event-driven, and scalable technologies including Lambda, EMR, SNS, SQS, Kafka, & Redshift. Transformed enterprise-wide ABaC systems to leverage serverless solutions, enabling on-demand scalability, API fronted, and a NoSQL backend.• Led the design, development, and delivery of cloud-based Machine Learning models for a leading Healthcare organization, utilizing Databricks on Azure. This encompassed the development of over 50 automated & scalable Data/ML pipelines, handling more than 100TB of data. We established a feature store, streamlined orchestration, one-click automation, Fast API-based data sharing, & integration with MLOps for effective model management, tracking, & drift detection.• Developed and deployed Unstructured Data Analytics for a worldwide Insurance provider, employing AWS serverless technologies like API Gateway, EKS, Apigee, Lambda, & ES. Built solutions for real-time gathering and processing of Unstructured Data, building business processes such as damage detection & and claims redaction. Within the first few months, our solution led to millions of dollars in business value and enabled enhanced claims processing with contract values of over $500M. • Migrated multiple production environments for a leading heavy machinery manufacturer from Cloudera Hadoop on-premises to AWS native. This entailed architecture design, networking, firewall configuration, benchmarking, and security, leading to a fully serverless, transient, and scalable, using technologies like Snowflake, Lambda, EMR, ECS, & DynamoDB, ensuring high availability & event-driven operations.

• Architected cloud-centric solutions emphasizing key technology aspects like cloud scalability, high availability, microservices architecture, cost optimization, and cloud-native auditability. Pioneered cloud reference architectures, adhering to cloud best practices and standards for Accenture, evaluating and onboarding cloud-native tools and services.• Strategically selected and implemented cloud-based tools and services for the Accenture Insights Platform (AIP), focusing on cloud-oriented software and development methodologies suitable for distributed data processing in the cloud. Provided expert guidance in designing and deploying scalable, resilient cloud-native applications and data pipelines, ensuring seamless integration with cloud-based BI systems and compliance with cloud security standards.• Defined comprehensive cloud architectures to meet client requirements, setting up cloud infrastructures for various use cases. Formulated cloud-based solutions that offer optimal value, considering client-specific constraints, and balancing them against cloud-native principles and practices. Ensured solutions were fully integrated with clients' existing cloud BI systems, adhering to cloud security and compliance protocols.• Oversaw cloud operations, emphasizing data governance in cloud environments and validating data consistency across distributed systems. Ensured technical solutions delivered cloud-optimized performance, auto-scaling capabilities, and were built for high availability and disaster recovery, aligning with both Accenture’s cloud strategy and client cloud infrastructure.

• Played a key role in the design and development of Kogentix's Automated Machine Learning Platform (AMP) and Infrastructure Management Solution (KiMS), catalyzing the automation of data analytics and enhancing infrastructure management.• Oversaw AMP's deployment across cloud and on-premise Big Data platforms, such as Cloudera Hadoop and MapR, harnessing technologies like Spark and HBase, and directed operational management, data governance, and cost optimization strategies.• Created predictive models to forecast patient admissions for Europe's largest medical system, using Spark and HBase to streamline insurance claims processes, and designed a sophisticated analytics framework for a leading CPG company to refine marketing strategies with Cloudera Hadoop and SAP Hana.• Built a comprehensive IoT-based alarm correlation and predictive maintenance platform for a major power generation firm, utilizing Kafka and Spark streaming, and provided technical leadership in developing large-scale data solutions using distributed computing and NoSQL databases, guiding data engineers in enhancing machine learning model accuracy.

• Integral to the Big Data Solution Enablement team, creating and implementing end-to-end Hadoop-based analytics solutions, including Spark, MapReduce, and HBase, supplemented by custom web interfaces, automation via Oozie, shell scripting, and IoT device integration.• Established and standardized reference architectures and frameworks, advocated for the optimal selection of tools and techniques, and contributed to rigorous code reviews to maintain adherence to design and architectural standards.• Successfully facilitated migrations between Intel and Cloudera Hadoop distributions, ensuring seamless transitions with zero data loss and minimal downtime, alongside the deployment and optimization of Hadoop clusters across various client locations.• Engaged in the deployment, management, and tuning of Hadoop clusters, executing performance optimization, load balancing, and cluster health monitoring while providing solutions for user-related issues.

• Developed and implemented comprehensive Hadoop-based analytics solutions, employing technologies like MapReduce, Hive, Pig, and HBase, integrated with custom web interfaces and automated through Oozie and shell scripts.• Delivered Hadoop system support, addressing a range of needs from Unix administration to the development of Python streaming jobs, and led the deployment of Hadoop clusters, optimizing for performance and reliability.• Participated in global customer engagements including PoCs, workshops, and use-case development, and created a machine learning system with Cloudera Hadoop to analyze voter and donor patterns to enhance campaign strategies. Additionally, architected data workflows encompassing batch and real-time data ingress, transformation, and egress for comprehensive analytics lifecycle management.

• Worked with clients like Travelers Insurance and FLS Connect to build tailored Hadoop-based solutions, overseeing the full development cycle from requirements analysis and data processing to final implementation, utilizing MapReduce, Hive, Pig, and Sqoop for data handling and analytics.• Built a Cloudera Hadoop-powered machine learning system to analyze behavior patterns, enhancing voter and donor identification for campaign optimization, and architected a robust data architecture that facilitates both batch and real-time data integration, transformation, and distribution to downstream analytics and visualization platforms.

• Developed EBSCO's admin web interface testing by automating test procedures, crafting and integrating new test cases into the current suite, and implementing routine smoke tests for system verification.• Enhanced EBSCO's automated testing framework by engineering and deploying new C# automated test cases, ensuring seamless amalgamation with the pre-existing suite, and conducting regular maintenance through smoke testing protocols.

• Engineered Chain Reaction web services, facilitating key operations in Product Lifecycle Management, Manufacturing, and Retail/Web Sales, along with Point of Sale (POS) interactions.• Implemented systems utilizing C#, ASP.NET, HTML, and SQL Server to create a robust, integrated end-to-end business management system.