Can you highlight a specific project and explain a use case that uses different data engineering technologies?

In a recent project I led the migration of a legacy ETL pipeline to a modern data lake architecture. The goal was to ingest, transform, and serve data for a real‑time analytics dashboard. We used Apache Spark on Databricks for distributed processing, writing UDFs in Python to clean and enrich data. Raw data from various sources (Kafka streams, REST APIs, and on‑premise databases) were ingested into Azure Data Lake Storage Gen2. We leveraged Azure Data Factory to orchestrate the workflow, scheduling nightly batch jobs and triggering Spark jobs via the Databricks REST API. For data warehousing, we used Azure Synapse Analytics, loading processed data into columnar tables for fast query performance. The solution reduced data latency from 24 hours to under 10 minutes for streaming data, improved data quality through automated validation, and cut storage costs by 30% by moving infrequently accessed data to cheaper tiers. This project showcased my ability to integrate multiple cloud services, optimize performance, and deliver business value.