Mukesh Kumar K R

Designed and built an AWS-based data platform that brings transactional and product activity events into Amazon Redshift for operational analytics and executive reporting. Developed modular dbt models across staging and dimensional layers to support governed reporting datasets. Created fault-tolerant ELT workflows using MWAA (Airflow) and S3 with alerting and auto-recovery, and standardized infrastructure with Terraform. Improved Redshift query performance by tuning physical design choices to reduce BI dashboard load times.

Ingested, transformed, and modeled large multi-terabyte datasets for supply chain and marketing reporting. Built Python and SQL ingestion pipelines from semi-structured survey data in AWS S3 into Amazon Redshift. Implemented dynamic consent tracking logic to support real-time opt-in/opt-out compliance without full table rebuilds. Developed and optimized distributed PySpark transformations on Amazon EMR and automated workflows with MWAA and Spark to reduce manual reconciliation.

Developed a Python debugging and analysis tool using AST-based static inspection. Added AI-assisted logic to detect likely syntax and logical issues, plus anomalous execution patterns and edge cases. Focused on assisting developers by automatically highlighting probable problems in Python scripts.

Built an end-to-end RAG workflow that ingests unstructured documents, splits them into chunks, and indexes them for semantic retrieval. Used a vector database to store embeddings and enable high-accuracy question answering over the document library. Integrated LLM-based generation for answering and connected the pipeline components end to end.