Ramya Ravi

I’m a Data Engineer with 4+ years of experience building and optimizing scalable ETL/ELT pipelines across multi-cloud environments. I focus on delivering SLA-driven, business-critical datasets while improving query performance and reducing cloud compute costs in production.

In my work, I design and maintain cloud-native pipelines using Python and PySpark, and I specialize in BigQuery performance tuning through partitioning and clustering plus SQL query optimization. I’ve reduced processing time by ~30% and engineered high-volume batch ingestion workflows across Azure Data Lake and GCS feeding BigQuery analytical models.

I also drive data modernization efforts by aligning AWS storage/ETL concepts (S3, Glue) with GCP-native analytics solutions. From orchestrating workflows with Apache Airflow DAGs and containerized deployments in Docker to monitoring and troubleshooting production pipelines, I ensure data quality via structured validation checks, secure access using IAM, and reliable releases through test cases and CI/CD practices.