Nandini ARJUNAN

– Architected a real-time streaming data ingestion pipeline for biosignal data (ECG, SpO2, IMU) using Azure Event Hub enabling low-latency processing and scalable cloud storage. – Built sliding window feature engineering pipelines for real-time data transformation, significantly improving throughput for downstream ML consumption. – Orchestrated end-to-end data workflows using Apache Airflow, automating data ingestion, preprocessing, validation, and delivery steps. – Containerized pipeline components using Docker with CI/CD integration on Azure, enabling reproducible and automated deployments.

– Built and maintained real-time ETL pipelines for sales, inventory, and demand forecasting data using Python, SQL, and Apache Airflow processing high volumes of structured data reliably. – Designed dimensional data models and transformation logic to support centralized data warehousing and BI reporting. – Integrated data from multiple heterogeneous sources, enforcing data quality checks and governance standards across pipelines. – Automated data workflows and reporting pipelines, reducing manual processing time and contributing to 10-15% operational cost reduction.