I build data-driven software, cloud-native systems, and ML solutions that turn data into practical insights.
Contributed to production ETL migration workflows by migrating 20+ warehouse tables across Medallion layers, validating data quality using SQL-based reconciliation scripts, and troubleshooting pipeline failures. Automated column-level mapping and metadata generation with Python, reducing manual effort while improving migration accuracy and operational efficiency.
Worked on a serverless cloud pipeline for log ingestion and processing, built monitoring dashboards for service health analysis, and contributed to deployment automation using IaC and GitHub Actions.
Built a DNS threat-detection pipeline using Zeek, FastAPI, and Random Forest ML to analyze raw PCAP traffic and detect DNS exfiltration attacks with 98% malicious recall, leveraging advanced telemetry features like Shannon entropy and subdomain analysis for protocol-aware forensic detection.
Developed an ML pipeline (including ETL) with S3 partitioning and Parquet, improved Athena query performance by ~70%, trained XGBoost on SageMaker and deployed real-time inference via FastAPI on EC2 with Docker. Serverless ETL reduced infra costs significantly.
Building production-ready data pipelines, cloud-native systems, ML applications, and foundational backend services.
Designing ETL pipelines, Medallion architectures, data transformation workflows, and analytics-ready datasets using SQL, Python, and modern data engineering practices.
Building cloud-native data platforms and serverless solutions on AWS, with hands-on experience across Azure services for monitoring, storage, and event-driven workflows.
Developing end-to-end ML workflows including preprocessing, feature engineering, model training, inference APIs, and deployment for real-world applications.
Building monitoring dashboards, analyzing metrics, and enabling data-driven decision making using cloud-native analytics and observability tools.
Led cross-functional technical projects: coordinated dev & design teams, conducted technical interviews, mentored juniors, and ensured code quality. Analyzed process improvements and managed financial operations.
Worked on modular Flutter components, performance tuning, and implemented self-check mechanisms to catch regressions early. Participated in code reviews to boost app stability.
I'm a Final-year B.Tech. Information Technology student at VIT Vellore, India, specializing in Data Engineering, Cloud Computing, and Machine Learning. My work focuses on building production-ready data pipelines and cloud-native ML systems, covering the complete lifecycle from ETL and analytics to model deployment and real-time inference. Alongside this, I have foundational experience developing backend APIs and Flutter Apps.
