Sravya Danda

At Grainger, I build real-time retail data pipelines with Apache Kafka, Flink, and AWS Kinesis, enabling sub-second analytics for footfall, sales, and inventory movement. I also modernized cron-based ETL into Apache Airflow DAGs, reducing job failure rates by 60%.

Previously at Johnson & Johnson, I led migration of a legacy on-premise warehouse to AWS Redshift and built automated AWS Glue, Lambda, and Step Functions pipelines. My Redshift optimization work improved query performance by 50%, reduced storage costs by 20%, and contributed to $30K in annual savings.

I work across AWS, Azure, GCP, Snowflake, Spark, Hadoop, Kafka, Airflow, Python, SQL, and Scala to turn APIs, databases, files, and streaming events into analytics-ready datasets. I've built data lakes, warehouses, data marts, and governed ingestion frameworks for retail, healthcare, banking, and e-commerce workloads.

I partner closely with analysts, data scientists, product managers, and DevOps engineers in Agile teams. I care about reliable data quality, observability, cost-efficient infrastructure, secure access, and documentation that supports production readiness.