Mid-level Data Engineer specializing in multi-cloud real-time data pipelines
California, USAData Engineer5 years experienceMid-LevelHealthcareHealthcare ITClinical Research
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About
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.
Experience
Data EngineerMolina Healthcare
ML Application EngineerEgen
Associate Data EngineerPersistent Systems
Education
University at Buffalomaster, Computer Science (AI/ML) (2025)
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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