Navya Sandrana

I’m a Databricks-Certified Data Engineer with 8+ years designing, developing, and maintaining scalable ETL/ELT pipelines and data workflows. I build governed, production-grade datasets using Python, Apache Spark, SQL, and orchestrate end-to-end pipelines with Apache Airflow.

In my current role, I engineer AWS-based architectures that process 40M+ daily events with 99.7% data accuracy. I optimize Redshift and lake storage (S3/Parquet/partitioning) and use Spark tuning to reduce average query runtime by 40% and cut cloud compute spend by 25%.

I also lead real-time ingestion with Kinesis and Lambda, achieving sub-60-second data latency for streaming workloads. I embed data governance and observability—schema drift detection, anomaly alerting, reconciliation, lineage, RBAC, and HIPAA-grade handling—detecting 15+ data quality incidents early and reducing mean time-to-resolution by 40%, while delivering faster releases with Terraform and CI/CD.