Data Practitioner specializing in architecting resilient ETL/ELT data pipelines, designing predictive Machine Learning models, and orchestrating scalable MLOps workflows. Committed to engineering high-throughput data systems that drive measurable business intelligence.
01 // Distributed ETL Pipeline Architecture (PySpark / PostgreSQL / Airflow)
Domain: Data Engineering & Distributed Systems
Challenge: High-latency ingestion and transformation across multi-source batch data without unified data quality checks.
Architecture:
- Distributed transformations engineered with Apache Spark (PySpark) for high-throughput partitioning.
- Normalized dimensional data warehouse modeled in PostgreSQL.
- Automated workflow scheduling, dependency handling, and retry policies managed via Apache Airflow.
Key Impact: Reduced batch processing runtime by 65% with automated data validation and zero data loss.
View Data Engineering Repositories
02 // End-to-End Predictive ML & MLOps Lifecycle (PyTorch / MLflow / DVC / FastAPI)
Domain: Machine Learning & MLOps Infrastructure
Challenge: Eliminating training-serving skew, tracking model version drift, and enabling reproducible experiment pipelines.
Architecture:
- Versioned datasets and preprocessing pipelines using DVC (Data Version Control).
- Model lifecycle tracking, hyperparameter tuning, and artifacts stored in MLflow Model Registry.
- Low-latency RESTful inference microservice built with FastAPI and packaged in Docker.
Key Impact: Achieved 94.2% ROC-AUC with automated continuous integration and seamless model rollback capabilities.
View Machine Learning Repositories
03 // Executive Analytics & Decision Intelligence (Power BI / SQL / Looker Studio)
Domain: Business Intelligence & Data Storytelling
Challenge: Siloed operational data across disparate databases preventing cross-functional executive visibility.
Architecture:
- Built robust star-schema data models and advanced DAX measures for real-time KPI aggregations.
- Interactive, high-performance dashboards featuring multi-tier drill-downs and automated alert triggers in Power BI & Looker Studio.
Key Impact: Consolidated 5+ disparate reporting sources into a unified real-time dashboard, cutting ad-hoc reporting time by 80%.
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