Tell me about your recent project.

My most recent project was building a real‑time inventory analytics platform for a national retailer. The goal was to ingest transactional data from multiple point‑of‑sale systems, process it in near real‑time, and deliver dashboards that enable store managers to monitor stock levels and forecast demand. I designed the architecture using Kafka for ingestion, Spark Structured Streaming for transformation, and Snowflake as the data warehouse. I implemented data quality checks using Great Expectations and automated the pipeline with Airflow. The solution reduced data latency from 24 hours to under 5 minutes, increased data accuracy by 15%, and enabled the business to make faster inventory decisions. I also set up monitoring dashboards in Grafana and integrated alerts for pipeline failures. The project required close collaboration with the product team, data scientists, and operations, and it was delivered on schedule with a 10% budget underrun.