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Tier-1 brand, mid-level popular ML role, metro location, and broad cross-functional skillset increase applicant competition.
Strong healthcare/biopharma data and EHR/claims experience requirement limits cross-industry transferability.
Mandatory 4+ years, specific tech stack, healthcare data expertise and governance make screening stringent.
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Design, build, and maintain ETL/ELT data pipelines and workflows on Snowflake, Redshift, AWS, Apache Airflow for healthcare datasets.
Develop curated data models and feature datasets to support AI/ML model training, scoring, and business analytics processes.
Ensure data quality, governance, system reliability, and provide ongoing operational support for data platforms in healthcare/biopharma context.
Bachelor's or Master's degree in quantitative field (e.g., Computer Science, Data Science, Engineering, Information Systems, Economics).
At least 4 years of relevant data engineering or analytics engineering experience.
Proficiency with Python, SQL, Snowflake, AWS services (S3, Lambda), Amazon Redshift, Apache Spark, Apache Airflow.
Experience with healthcare data sources (EHR/EMR, claims, laboratory data) and healthcare data standards.
Experienced individual contributor comfortable owning end-to-end data pipeline architecture and operational support in regulated healthcare environments.
Strong AI-native orientation with understanding of modern AI paradigms like Generative and Agentic AI, and capability to build data systems supporting autonomous workflows.
Ability to translate complex healthcare business and epidemiological contexts into scalable, secure, and compliant data engineering solutions.