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Rakesh Thota
Mid-level Data Engineer specializing in multi-cloud real-time data pipelines
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Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.
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Key Strengths
- Owned end-to-end clinical trial data pipeline processing 100TB+ monthly
- Implemented schema validation/data quality checks and schema drift detection to prevent silent downstream failures
- Improved reporting latency by 20% and reduced manual reporting effort by 40%
- Achieved 99.9% pipeline processing reliability via Airflow/MWAA tuning, alerting, retries, and idempotent design
- Designed resilient API ingestion with response validation, quarantine patterns, and alerting to prevent downstream table corruption
- Shipped production Flask REST APIs on Cloud Run with OAuth2, caching, BigQuery optimization, CI/CD, and URL-based versioning
- Managed breaking API changes by running parallel v1/v2 endpoints and deprecating safely with sunset headers
- Stood up an ambiguous early-stage RAG system for non-technical clinical researchers (LangChain + GPT-4) with monitoring and retrieval-quality evaluation
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