Data Engineer

About the Role: We are looking for a skilled Data Engineer with strong expertise in R and Python to support our healthcare client in building and maintaining robust data pipelines and analytical infrastructure. The ideal candidate will have experience working with healthcare datasets (EHR/EMR, claims, patient data) and a deep understanding of data integration, transformation, and governance within a regulated environment. Key Responsibilities: • Design, build, and maintain scalable data pipelines for ingestion, transformation, and processing of healthcare data (EHR, claims, lab, and patient data). • Develop ETL/ELT processes using Python and R to support analytics, reporting, and machine learning workflows. • Integrate data from multiple healthcare systems and APIs using standard formats (HL7, FHIR, CCD). • Collaborate with data scientists, analysts, and clinicians to ensure clean, reliable, and accessible data for research and analytics. • Optimize data workflows for performance, scalability, and reliability. • Ensure compliance with HIPAA and other healthcare data privacy regulations. • Develop scripts and automation for data validation, quality checks, and metadata management. • Work closely with cloud and DevOps teams to deploy and maintain data infrastructure in AWS/Azure/GCP environments. Required Qualifications: • Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or related field. • 3–6 years of experience as a Data Engineer or similar role. • Strong proficiency in Python (Pandas, PySpark, SQLAlchemy) and R for data manipulation and transformation. • Experience with SQL and relational databases (PostgreSQL, MySQL, SQL Server, etc.). • Working knowledge of data integration and ETL tools (Airflow, dbt, Talend, Informatica, etc.). • Experience handling healthcare data formats (HL7, FHIR, ICD-10, CPT). • Familiarity with data privacy and security standards (HIPAA, PHI). Preferred Qualifications: • Experience with cloud data platforms (AWS Redshift, GCP BigQuery, Azure Synapse). • Exposure to data lake / lakehouse architectures. • Knowledge of Spark, Databricks, or distributed data processing frameworks. • Familiarity with data visualization and reporting tools (Tableau, Power BI, or R Shiny). • Understanding of data governance, lineage, and metadata management frameworks. • Strong communication and collaboration skills in cross-functional healthcare teams.