Senior Data Engineer - Real World Data And Healthcare Analytics

Zifo; Chennai District; On-site; INR 1,500,000 - 2,700,000; Full time

Job summary

Zifo in Chennai, India is seeking a Data Engineer with Real World Data (RWD) expertise to build end-to-end pipelines for multimodal healthcare datasets. You will drive data ingestion, harmonization, and analytics layers to support AI/ML initiatives and evidence-generation studies.

Responsibilities include harmonizing clinical terminologies, supporting feasibility studies, and developing reusable data assets for model-ready datasets.

Qualifications

• 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.

Responsibilities

• 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.
• Harmonize heterogeneous healthcare data sources and coding systems into standardized formats.
• Map and transform clinical terminologies including SNOMED CT, ICD-10, LOINC, RxNorm, CPT/HCPCS.
• 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.
• 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.
• Strong experience with Python, SQL, Spark / PySpark; production-grade ETL/ELT pipelines; data modelling concepts; metadata management, lineage, monitoring, and governance.

Skills

Python

SQL

Spark / PySpark

Tools

Git

CI/CD pipelines

Unit testing & automation

Performance monitoring

Job description

Job Description

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
• SNOMED CT
• ICD-10
• LOINC
• RxNorm
• 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.