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High due to mid-level Data Engineer title, 4–8 years requirement, and metro location despite healthcare niche.
High because role requires healthcare RWD, OMOP/FHIR and clinical coding domain expertise.
High due to explicit 4–8 year requirement and mandatory healthcare data engineering and tooling expertise.
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Design, develop, and maintain scalable ETL/ELT pipelines for large healthcare and real-world datasets including structured and unstructured data.
Harmonize heterogeneous healthcare data sources and clinical terminologies (e.g., SNOMED CT, ICD-10, LOINC, RxNorm, CPT/HCPCS) into standardized formats supporting OMOP and FHIR data models.
Support advanced analytics, AI/ML initiatives, and evidence-generation by delivering analysis-ready datasets and collaborating with epidemiologists, biostatisticians, and data scientists.
4-8 years of experience in data engineering, healthcare analytics, or real-world data platforms working with large-scale healthcare datasets in regulated environments.
Strong experience in Python, SQL, Spark/PySpark, and building production-grade ETL/ELT pipelines.
Experience with cloud platforms such as AWS, Databricks, Snowflake, Palantir Foundry or equivalent and knowledge of data governance, lineage, metadata management.
Work Experience Required: 4-8 years; Notice period: Not explicitly mentioned in the JD.
Experienced with healthcare real-world data domains including Claims data, EHR, Registries, patient-reported outcomes, and digital health datasets.
Capability to implement and harmonize clinical terminologies and common data models such as OMOP and FHIR at scale.
Comfortable working across technical and business teams supporting observational research and healthcare analytics workflows with a systems thinking and delivery-focused mindset.