Can you dig deeper into what technologies you used, how the process works, and explain briefly what technology 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 design the pipeline in Airflow, where each task is a PythonOperator that calls a Spark job for heavy transformations. The data originates from REST APIs and CSV files stored in S3. I use Pandas for small datasets and PySpark for larger ones, performing cleaning, aggregation, and enrichment. Results are loaded into PostgreSQL, and I expose them via a REST API built with FastAPI for downstream consumption. I containerize the services with Docker, push images to Docker Hub, and deploy to a Kubernetes cluster using Helm charts. Continuous integration is handled by GitHub Actions, which run unit tests and linting. I monitor the pipeline with Prometheus, visualize metrics in Grafana, and set up alerting in PagerDuty. This workflow ensures data integrity, scalability, and rapid delivery of insights.