Monthly Water Meter Closing System
An enterprise-scale data processing architecture handling 15M+ monthly meter readings with Delta Lake and Databricks Runtime 10.4, reducing processing latency from 168 hours to 2 hours while implementing Zero-ETL patterns that eliminated data inconsistencies and generated €13M in annual operational savings.
Key Achievements
Distributed Processing Optimization
Engineered custom PySpark transformations with 8-node optimized clusters that process 15M records in 2 hours (97% faster), using dynamic allocation and spot instances for cost efficiency.
Data Pipeline Automation
Designed fault-tolerant pipelines with 42 Airflow DAGs and 178 tasks with comprehensive retry mechanisms, reducing manual interventions by 98% and enabling €13M annual operational savings.
Data Quality Framework
Implemented a multi-stage data validation framework with 85+ quality rules, schema enforcement, statistical anomaly detection, and automated reconciliation processes achieving 99.9% accuracy.