Senior Data Engineer - Real World Data And Healthcare Analytics

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.

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.

What Were Looking For

• Systems thinker who can work with complex and evolving datasets.

• Strong collaboration skills across technical and business teams.

• Agile mindset with a focus on delivery.

• Commitment to data privacy and ethics.