Vinod Kumar Vinod Boddupally B

About Vinod Kumar Vinod Boddupally B

I\'m a Data Engineer who bridges complex data systems with tangible business outcomes. Over 5+ years in payroll, IoT, and travel analytics, I\'ve built a track record of designing pipelines that don\'t just move data they drive efficiency, trust, and insight.My approach combines modern cloud architecture with AI-augmented engineering to solve real operational challenges. At Paychex, I developed AI-assisted ETL pipelines processing 12M+ payroll records daily, using LLMs to auto-detect schema drift improving stability and saving 15+ engineer-hours weekly. By integrating Kafka streams with SageMaker, I enabled real-time payroll error alerts that cut incident resolution from 4 hours to under 45 minutes across 8 systems.I engineer with accountability and precision. At Honeywell, I built Azure pipelines merging 9M+ IoT events daily, reducing latency from 6 hours to 90 minutes. Implementing LLM-based log classification accelerated root-cause discovery by 8 hours per incident. Earlier, at Airbnb, I automated ETL workflows for 30M+ daily records, improving reporting latency from 9.2s to 1.9s and uncovering $7M+ in new opportunities through dashboards I built for global teams.I specialize in:Scalable ETL/ELT pipelines using Spark, Airflow, Python & cloud platforms (AWS, Azure, Snowflake)Real-time streaming with Kafka and event-driven architecturesIntegrating AI/ML workflows (SageMaker, MLflow, LangChain) to automate validation, tagging, and anomaly detectionTurning data into clear insights through Power BI, Tableau, and collaborative storytellingI\'m passionate about building data infrastructure that is robust, intelligent, and trusted—enabling teams to operate with greater speed and confidence. Let\'s connect if you\'re interested in data innovation, scalable engineering, or the future of AI-powered data systems. Open to conversations and collaborations: v••••••••@gmail.com

Experience

1. Data Engineer

   Paychex

   Jun 2025 — Present · US

   Developed AI-assisted ETL pipelines using AWS Glue, Snowflake, and Python, processing 12M+ payrollrecords daily while using LLMs to auto-detect schema drift, improving load stability and saving 15 engineer-hours per week through analytical problem-solving. Integrated Kafka streams and Airflow DAGs with anomaly-detection models in SageMaker, enabling real-time payroll error alerts across 8 enterprise systems and cutting incident resolution time from 4 hours to under 45 minutes through decisive collaboration. Deployed PySpark-based data validation enhanced with LLM prompt logic, auto-generating QA scripts that reduced data quality issues by 1,200 per month and strengthened stakeholder trust through accountability. Implemented metadata enrichment with LangChain embeddings and AWS Glue Catalog, allowing semantic tagging of 2TB+ daily data, improving governance traceability across 7 business domains with initiative and precision

Education

• Sphoorthy Engineering College

  Mechanical Engineering

• Campbellsville University

  Master's degree, Information Technology