Batch jobs ran nightly, causing data latency of 12+ hours

Implemented Airflow DAGs with incremental loads, reducing latency to under 1 hour

Manual script maintenance caused frequent failures

Automated pipeline testing and monitoring, achieving 99.9% success rate

Data processing cost exceeded budget by 30%

Optimized Spark jobs and leveraged spot instances, cutting costs by 25%

Pipeline lacked error handling, leading to data gaps

Added retry logic and alerting, eliminating data gaps

Team spent 20% of time on pipeline debugging

Introduced data observability tools, reducing debugging time by 60%