Can you explain any of the recent projects you have worked on that involved water extraction?

In my most recent role, I led a data pipeline project that extracted water consumption data from a network of IoT sensors deployed across a municipal water distribution system. The goal was to provide real‑time analytics for leak detection and usage forecasting. First, we designed a Kafka-based ingestion layer to handle the high‑velocity sensor streams, applying schema validation and deduplication. Next, we used Apache Spark to perform batch transformations: cleaning missing values, normalizing units, and aggregating consumption at the meter, neighborhood, and city levels. The cleaned data was then loaded into Amazon Redshift for downstream reporting. We scheduled nightly vacuum and analyze operations to maintain query performance. For visualization, we built dashboards in Tableau that highlighted anomalies and trends. The project reduced leak detection time by 40% and improved forecasting accuracy from 70% to 88%. Key lessons included the importance of robust schema evolution handling and the value of automated data quality checks. This experience showcases my ability to architect end‑to‑end data solutions, work with cross‑functional teams, and deliver measurable business impact.