Nikith Neelisetty

“Data/analytics engineer who stood up foundational pipelines and services at Meta for the Ray-Ban Meta launch—building a retailer sales ingestion system (S3/Hive) with rigorous DQ checks, 1-day SLAs, and dimensional rollups used by GTM to track sales trends. Also built a modular multi-retailer web-scraping system for out-of-stock alerts and shipped internal GraphQL APIs and an n8n-like workflow builder using serverless (AWS Lambda) with strong testing and observability practices.”

“Data engineer focused on large-scale ETL/ELT pipelines across cloud stacks (GCP and AWS), including Spark-based transformations and orchestration with Airflow. Has experience loading up to ~2TB per BigQuery target table and designing atomic loads to multiple downstream systems (Elasticsearch + Kafka), with Kubernetes deployment and Jenkins CI/CD.”