Tell me about a project you worked on and how you approach the design and implementation.

Sure. In my most recent role, I was part of a team building a real‑time analytics dashboard for a retail client. The project required ingesting click‑stream data from Kafka, transforming it, and loading it into Snowflake for ad‑hoc reporting. My responsibility was to design the data pipeline and oversee its implementation. I started by defining clear data contracts and schema versioning to avoid downstream issues. I chose Apache Airflow to orchestrate the ETL jobs, using Python operators for data extraction and Spark for heavy transformations. I also implemented unit tests for each transformation step and set up CI/CD pipelines to deploy DAGs automatically. During the implementation phase, I focused on modularity: each step was encapsulated in a reusable component, making it easier to maintain and extend. The result was a pipeline that processed 10 million events per day with 99.9% uptime and a 30% reduction in data latency compared to the legacy system. This experience taught me the importance of clear contracts, modular design, and automated testing in building reliable data pipelines.