Can you dig deeper into what technologies you used, how the process works, and explain briefly what technologies you have used or what kind of work you have done on a daily basis, and what kind of data or transformations you have dealt with?

I start by defining the data sources and requirements, then design an Airflow DAG that orchestrates the workflow. The DAG pulls data from APIs and S3, uses Python operators to run Spark jobs for large‑scale transformations, and writes results to a PostgreSQL warehouse. I employ Docker Compose to spin up local environments for testing. For data quality, I write unit tests with PyTest and use Great Expectations to validate schemas. Transformations include deduplication, type casting, and feature engineering for machine learning models. I schedule jobs to run nightly, monitor them via Grafana dashboards, and set up Slack alerts for failures. This end‑to‑end process ensures reliable, repeatable data pipelines that feed analytics and reporting tools.