What is your hands‑on experience with data pipelines?

I have built end‑to‑end data pipelines using Airflow for orchestration, Spark for distributed processing, and Kafka for real‑time ingestion. My pipelines ingest raw logs from cloud storage, transform them with Spark jobs (filtering, aggregation, enrichment), and load the results into Snowflake or a data lake. I design DAGs with clear dependencies, set retry policies, and use XComs for lightweight data passing. For monitoring, I enable Airflow’s alerting and integrate with Prometheus/Grafana for metrics. I implement data quality checks using Great Expectations, and maintain lineage via metadata catalogs. For fault tolerance, I use checkpointing in Spark and idempotent writes. I also automate incremental loads with Snowpipe and schedule batch jobs during off‑peak hours. This approach ensures reliability, scalability, and observability across the pipeline.