About The Role

We are looking for a highly skilled Data Engineer with Real World Data (RWD) experience to build and manage end-to-end data pipelines for multimodal healthcare datasets. The ideal candidate will work at the intersection of data engineering, analytics, and healthcare research, transforming complex healthcare data into analysis-ready assets that support advanced analytics, AI/ML initiatives, and evidence-generation studies. This role requires expertise in large-scale healthcare data processing, data harmonization, cloud platforms, and modern data engineering practices.

Responsibilities

Data Engineering & Pipeline Development

Design, develop, and maintain scalable ETL/ELT pipelines for large healthcare and real world datasets.
Build and manage data ingestion, transformation, harmonization, and analytics layers.
Implement data quality frameworks, governance controls, lineage tracking, and monitoring.
Manage data lifecycle processes across raw, curated, and analytics-ready environments.
Work with structured and unstructured healthcare datasets from multiple sources.

Healthcare Data Harmonization

Harmonize heterogeneous healthcare data sources and coding systems into standardized formats.
Map and transform clinical terminologies including

o SNOMED CT
o ICD-10
o LOINC
o RxNorm
o CPT/HCPCS

Support implementation of common data models such as OMOP and FHIR.

Analytics & Study Support

Support data feasibility assessments and data quality evaluations.
Collaborate with epidemiologists, biostatisticians, data scientists, and business stakeholders.
Develop reusable data assets, cohorts, and model-ready datasets.
Enable advanced analytics and AI/ML use cases through reliable data engineering practices.

Application & Platform Development

Contribute to analyst-facing applications, dashboards, and self-service data products.
Support development of data products using modern workflow automation and AI assisted engineering approaches.
Provide guidance on efficient querying and optimization of large longitudinal datasets.

Requirements

Data Engineering

Strong experience with Python, SQL, Spark / PySpark
Experience building production-grade ETL/ELT pipelines.
Strong understanding of data modelling concepts - Star schema, Snowflake schema, Normalization and denormalization
Experience with metadata management, lineage, monitoring, and data governance.

Platforms & Technologies

Experience in one or more of the following - Palantir Foundry, Databricks, Snowflake, AWS or equivalent cloud platforms, HPC environments

Containerized workloads Software Engineering Practices
Git
CI/CD pipelines
Unit testing and automation
Performance monitoring and optimization

Domain Expertise

Candidates should have working knowledge of Healthcare Real World Data (RWD), Claims data, Electronic Health Records (EHR), Registries, Patient-reported outcomes, Wearables and digital health datasets

Understanding of study feasibility, observational research, and healthcare analytics workflows is highly desirable.

AI & Automation Experience

Preferred experience with Large Language Models (LLMs), AI-assisted data engineering, Agentic workflows, Data profiling and automated data quality assessments, integration of ML outputs into production data pipelines

Qualification / Requirement

4-8 years of experience in data engineering, healthcare analytics, or real-world data platforms.
Experience working with large-scale healthcare datasets in regulated environments.
Strong problem-solving and analytical skills.
Excellent stakeholder communication capabilities.
Formal educational qualifications are flexible; relevant experience and expertise are valued.

Nice to Have

Experience with multimodal healthcare datasets (clinical, omics, imaging, genomics, proteomics, microbiome, etc.).
Hands-on experience implementing OMOP/FHIR at scale.
Experience building self-service applications and data products for business users.
Familiarity with federated data networks and data quality frameworks.