Manish Chandra Matcha

The Data Analytics Application for Australia's NHMRC (National Health and Medical Research Council) was developed to streamline the analysis of large volumes of health research data. The project involved designing and implementing a robust data analytics platform that integrates various data sources, including research publications, funding data, and clinical trial results. Utilizing AWS services like ECS, Lambda, S3, and Redshift, the platform provides scalable processing capabilities, enabling dynamic scaling of data workloads.

Key features of the application include real-time data ingestion, automated data cleansing, and advanced analytical tools that support predictive modeling and trend analysis. The application uses machine learning algorithms to identify patterns and insights that help researchers and policymakers make data-driven decisions. The project significantly reduced data processing time by 60%, enhanced data accessibility through a user-friendly interface, and improved overall data governance with robust security and compliance measures using AWS IAM and VPC configurations. This solution has empowered NHMRC to make more informed decisions and streamline their research processes.

I developed an eCommerce back-end application that scores the content of each product (e.g., title, images, descriptions, attributes, specifications, and reviews). This score is useful in automating the content selection for each product, and it can be used as a quality check for enhancing the content.

Gateway LinkUp at Societe Generale was developed to integrate front-office banking applications with back-office systems, streamlining processes for cash transfers, equity transactions, and money markets. The project aimed to create a unified platform that enabled real-time data synchronization and efficient transaction handling across the private banking division.

Using Java Spring Boot, Apache Camel, and IBM MQ series, the project established seamless communication between systems, reducing manual intervention and errors. The architecture supported automated workflows for transaction validation, compliance checks, and data routing, providing end-to-end visibility of transaction statuses.

Gateway LinkUp significantly improved operational efficiency by reducing processing times and enhancing data accuracy. The platform’s scalable and adaptable nature allowed it to meet evolving business needs, playing a crucial role in modernizing transaction processing and enhancing service delivery in Societe Generale’s private banking sector.

The Product Pricing and Offers Solution at Naehas leverages a robust tech stack, including AWS Batch, Athena, Step Functions, Lambda, ECS, and MongoDB, to help businesses efficiently manage and optimize their pricing strategies and promotional offers. This solution provides an end-to-end platform for creating, deploying, and managing dynamic pricing models and personalized offers in real time, enabling companies to respond swiftly to market shifts and customer behaviors.

AWS Batch and ECS are utilized for scalable processing of large datasets, ensuring that complex pricing algorithms and rules can be executed efficiently. AWS Step Functions and Lambda are employed to orchestrate workflows and automate tasks, enabling seamless integration of different processes within the platform. Athena is used to query large datasets, allowing for real-time insights into market trends and customer preferences. MongoDB is the primary data store, supporting flexible and high-performance data management.

This integrated solution supports dynamic pricing adjustments, personalized offers, and advanced analytics, allowing businesses to continuously make data-driven decisions and refine their strategies.