Headline

Building Data Pipelines & ETL Systems @ Amazon | Python, Spark, SQL, AWS | 8+ Years | Open to Data Engineer Roles

About

I build production data infrastructure at Amazon, specializing in high-volume ETL pipelines, process automation, and data-driven optimization. WHAT I BUILD: I design and deploy distributed data systems that process millions of daily transactions: • Data Pipelines: Engineered 25+ production ETL workflows handling 50M+ records daily with 99.9% reliability • System Optimization: Transformed critical pipeline performance from 142 minutes to <60 seconds (99.3% improvement) through query optimization and distributed processing • Automation Infrastructure: Built end-to-end Python frameworks eliminating 715 manual hours weekly across operations teams • Cost Intelligence: Developed data-driven optimization systems generating $2.6M in annual savings through improved resource allocation TECHNICAL IMPACT AT SCALE: • Led data platform migration for 35 Python automation scripts and 25 ETL pipelines • Architected real-time tracking systems serving 500+ concurrent users with <2s latency • Designed alerting infrastructure processing 100K+ daily events for logistics operations • Built data quality frameworks improving accuracy from 75% to 98.5% TECHNICAL STACK: Languages: Python (Pandas, PySpark, NumPy), SQL (PostgreSQL, MySQL), Bash Data Engineering: Apache Spark, Apache Airflow, ETL Design, Data Modeling, Pipeline Orchestration Cloud & Infrastructure: AWS (S3, Glue, EMR, Athena, Redshift, Lambda), Docker, Git Data Warehousing: Dimensional modeling, Star schema, Data quality frameworks Tools: Kafka, Tableau, QuickSight, Jupyter, dbt BACKGROUND: My official title at Amazon is Business Analyst, but I spend the majority of my time on data engineering work—building ETL pipelines, architecting data systems, and automating data infrastructure. I'm seeking opportunities where my role aligns with the technical work I'm passionate about. Currently seeking Senior Data Engineer roles at companies that value technical excellence, collaboration, and building systems that genuinely serve users. Let's connect if you're building data infrastructure that powers critical business decisions.

Location

Hyderabad

AI stack

claude

codex

cursor

deepmind

gemini

grok

Work experience

Senior Business Analyst - Data Intelligence at Amazon, Oct 2021 - Present · 4 yrs 11 mos, Hyderabad, Telangana, India, Building production data infrastructure, ETL pipelines, and automation systems for Amazon's logistics operations PIPELINE ARCHITECTURE & OPTIMIZATION: • Architected and deployed 25+ production ETL pipelines processing 50M+ daily logistics records, reducing data latency from 4 hours to near-real-time (<5 min) through incremental processing and partitioning strategies • Re-engineered critical VP-facing data pipeline using query optimization and parallel processing, improving execution time by 99.3% (142 minutes → 60 seconds) while maintaining data integrity across 10TB+ datasets • Led comprehensive data platform migration to Andes 3.0, encompassing 35 Python automation scripts, 25 ETL workflows, and 5 QuickSight datasets—improved pipeline maintainability and reduced infrastructure costs by 30% AUTOMATION & SYSTEM DESIGN: • Designed and implemented end-to-end Python automation framework for Proof of Delivery (POD) document generation, eliminating 400 manual hours weekly and reducing shipper attrition by 70% through reliable document workflows • Built real-time shipment tracking and alerting system serving 500+ SMB shippers, processing 100K+ daily events with automated ETA-based notifications—eliminated 315 hours/week • Engineered Python-based alert and case management system for trailer operations, reducing defect rates from 250 to 120 basis points YoY through automated hourly monitoring and intelligent routing DATA-DRIVEN OPTIMIZATION: • Developed optimization algorithm for carrier time-block allocation using Python and statistical modeling, recovering excess capacity and generating $2.6M annual savings ($50K/week). • Built predictive analytics dashboard for trailer replenishment using historical demand patterns, optimizing fleet logistics and reducing premium third-party carrier costs by $510K annually Technologies: Python (Pandas, NumPy, Scikit-learn), SQL (Redshift, PostgreSQL), AWS (S3, Glue, Athena, Lambda, QuickSight), Apache Airflow, Git, Tableau

Transportation Analyst at Amazon, Oct 2017 - Oct 2021 · 4 yrs 1 mo, Greater Hyderabad Area, Data pipeline development, automation engineering, and analytics infrastructure • Engineered automated case management and routing system using Python, reducing average resolution time by 68% (55.8 → 17.5 hours) and reallocating 6 FTE from monitoring to high-value analysis through intelligent workflow design • Architected real-time Track & Trace dashboard for Air Freight operations, implementing automated order-arrival matching algorithms that reduced average handling time by 25% across 200+ daily shipments • Built Electronic POD (EPOD) automation pipeline that improved documentation compliance by 900 basis points, achieved 100% on-time POD submission for inbound loads, and prevented 4,100+ delayed submissions through automated workflows • Developed comprehensive SQL-based analytics frameworks and query optimization strategies that improved decision-making speed by 30% across logistics operations • Analyzed complex multi-source datasets (shipment, inventory, capacity data) to identify operational bottlenecks, resulting in 15% reduction in operational costs through targeted interventions • Implemented KPI tracking and performance monitoring systems using automated data pipelines, contributing to 25% revenue increase through optimized resource planning • Built real-time productivity monitoring system tracking Non-Productive Time (NPT) across shifts with 30-minute alert intervals, reducing NPT by 50% (4% → 2%) through automated anomaly detection Technologies: Python, SQL, AWS (S3, Redshift, QuickSight), Tableau, Excel VBA

Marketing Analyst | Data Analysis, Market Research, and Strategic Planning at Qrius (formerly The Indian Economist), Jun 2017 - Aug 2017 · 3 mos, Hyderabad, Telangana, India, • Conducted market research and data analysis to identify customer trends • Built analytical reports translating complex datasets into actionable business recommendations

Education

Data Scientist Nano Degree, Edwisor

Bachelor of Technology - BTech, Electrical, Electronics and Communications Engineering, JNTUH College of Engineering Hyderabad