Architecting real-time pipelines, building ML models, and transforming data into insights
Real-world solutions across data engineering, ML, and analytics
Built a high-throughput data pipeline processing millions of events daily using Apache Spark and Kafka. Implemented auto-scaling on AWS to handle peak loads with 99.99% uptime.
Developed ensemble machine learning models (XGBoost, LightGBM) for revenue forecasting with 94% accuracy. Deployed as API service with real-time predictions.
Created comprehensive Tableau dashboards analyzing customer behavior, product performance, and revenue trends. Self-service analytics reduced report requests by 60%.
Designed scalable ETL framework using Apache Airflow and dbt. Automated data transformation pipeline with monitoring, error handling, and data quality checks.
Led migration of on-premise data warehouse to AWS Redshift with zero downtime. Optimized queries and implemented columnar compression reducing costs by 40%.
Built data governance framework with automated quality checks, lineage tracking, and metadata management using Apache Atlas and custom Python pipelines.
Proficient across modern data stack and cloud platforms
8+ years of progressive experience in data
Tech Company
Leading data infrastructure and platform initiatives
Analytics Startup
Building machine learning products and data analytics
E-commerce Company
Analytics platform development and business intelligence
Financial Services
BI development and data warehousing
Tech Startup
Getting started with data analysis and visualization
Sharing knowledge on data engineering, ML, and analytics
A deep dive into architectural decisions, bottleneck identification, and optimization techniques used to handle massive event throughput with Apache Spark and Kafka.
Lessons learned from deploying 10+ ML models: choosing the right frameworks, handling model drift, A/B testing strategies, and monitoring in production.
Practical strategies for optimizing Redshift costs including query optimization, data partitioning, compression techniques, and workload isolation.
Framework for building robust data quality checks using Python and Airflow. How to define SLAs, catch data issues early, and maintain data trust.
Advanced SQL optimization techniques: query plans, index strategies, statistics, and real-world examples that reduced query times by 90%+.
Building scalable analytics platforms: governance models, semantic layers, performance optimization, and strategies for 40K+ daily active users.
Open to collaboration, opportunities, and discussing all things data
Interested in working together on data projects or want to discuss your pipeline architecture?
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