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Dhanush Chandra Sekhar
Junior Data Engineer specializing in data pipelines and streaming ingestion
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Backend/data platform engineer who owned a near-real-time patient feedback ingestion system, building a FastAPI + Kafka service with Snowflake/Airflow orchestration. Demonstrates strong production Kubernetes/GitOps practices on AWS EKS (Helm, Argo CD, Sealed Secrets) and solved real-time data integrity issues via idempotent processing with Redis.
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Key Strengths
- End-to-end ownership of near-real-time patient feedback data pipeline (FastAPI + Kafka + Snowflake + Airflow)
- Designed idempotent Kafka consumer processing to prevent duplicate event counts using Redis message-ID cache
- Kubernetes deployment experience on AWS EKS using Helm for multi-service stacks (FastAPI, Redis, Airflow)
- Implemented GitOps with Argo CD as single source of truth; enabled fast rollbacks via git revert and audit trail
- Improved reliability by eliminating manual production kubectl changes and enforcing Git-based change control
- Secret management hardening using Bitnami Sealed Secrets committed safely to Git
- Incremental migration of legacy batch workflows to Airflow on EKS using Terraform with fallback strategy
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“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”
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