Aravind Suresh

Architecting a cloud data warehouse pipeline on Redshift and S3, orchestrating database and file-based feeds through Dataiku with proactive freshness and completeness validation gates that keep enterprise leadership safety and compliance KPIs trustworthy. I also spearhead the migration of on-premises data workloads to AWS — re-architecting legacy EC2-based processes into event-driven serverless pipelines with all infrastructure managed as code in Terraform, cutting operational cost while keeping the cutover zero-downtime.

Engineered end-to-end ML data pipelines ingesting tens of gigabytes a day for automated safety compliance review, and led the transition from a multi-channel CNN model to a hybrid multi-agent orchestration system that sharply reduced manual review cycles. I also designed and owned a suite of end-to-end analytics pipelines and dashboards on Tableau and Spotfire, serving hundreds of users and giving senior leadership near-real-time visibility into critical infrastructure across global operations.

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.