Aravind Suresh

Building and maintaining cloud-native data infrastructure on AWS, including data warehouse pipelines on Redshift and S3 that power enterprise-wide compliance reporting. My work spans the full pipeline lifecycle — from migrating legacy on-prem workloads to serverless architectures, to designing ML data systems that support automated safety compliance review using a hybrid approach combining LLM agents with traditional ML models. I also own a suite of analytics pipelines and dashboards serving hundreds of users across global operations, giving leadership near-real-time visibility into critical infrastructure.

Built an end-to-end automated data lineage solution that traced column-level dependencies across the full data stack — Databricks Workflows, Airflow DAGs, Redshift, and Hive Metastore. The system automated upstream and downstream lineage scanning, stored results in a database, and surfaced them through dashboards, dramatically compressing the time needed for root cause analysis when pipeline issues arose.