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Tier-1 employer, metro location, mid-level generalist ML/data role drives high competition.
Heavy healthcare data, EHR/claims expertise and regulated compliance needs reduce cross-industry transferability.
Explicit 4+ years plus mandatory cloud, data engineering, healthcare data and ML tool requirements increase strictness.
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Design, build, and maintain ETL/ELT data pipelines and platform components across Snowflake, Redshift, AWS, and Apache Airflow ensuring reliability and cost efficiency.
Integrate and curate diverse healthcare datasets (EHR, claims, lab data) with data quality, profiling, and lineage for advanced AI/ML model development and analytics.
Provide operational support, governance, and automation for data solutions enabling scalable AI-first analytics and commercial healthcare insights.
Bachelor's or Master's degree in quantitative field (Computer Science, Data Science, Engineering, Information Systems, or Economics).
4+ years experience in data engineering or analytics engineering supporting AI/ML workloads.
Proficiency with Python, SQL, and hands-on experience with Snowflake, AWS (S3, Lambda), Redshift, Apache Spark, and Apache Airflow.
Experience with healthcare data types (EMR/EHR, claims, lab) and data governance in healthcare/biopharma contexts.
Experience designing AI-first data systems supporting autonomous decision-making, including knowledge of Generative and Agentic AI paradigms.
Strong background in healthcare data engineering integrating complex clinical and commercial datasets for advanced analytics and ML model enablement.
Proven ability to manage cross-functional projects independently, applying rigorous data quality, performance tuning, and operational excellence practices in cloud environments.