Observational Health Data Analyst

Position: Observational Health Data Analyst Location: Remote (open to hybrid if preferred) Company: Johnson & Johnson Department: Global Epidemiology – Lupus Initiative Contract: 1-year contract with the possibility of extension About the Role Johnson & Johnson’s Global Epidemiology team is collaborating with a leading external data network on a major initiative focused on Lupus. We’re looking for an experienced Observational Health Data Analyst to help lead analyses across a diverse range of observational healthcare datasets. This role offers the opportunity to work on high-impact projects that directly inform scientific and strategic decisions within a global healthcare organization. You’ll partner closely with registry sites across Europe and internal experts in epidemiology, biostatistics, and data science to deliver actionable insights from real-world data. If you enjoy working with complex, real-world data and are motivated by the chance to make a meaningful impact on patient outcomes, this could be a great fit. Key Responsibilities • Lead the analysis of observational health data across a federated data network. • Conduct data characterization and quality assessments, and recommend improvements for data integrity. • Develop and apply statistical methodologies and programming techniques using R and SQL. • Collaborate with European registry sites, crafting and sending data queries to ensure clarity and accuracy. • Evaluate site-level results for consistency and provide recommendations for data refinement. • Use real-world data to answer research questions on the safety and effectiveness of treatments in the Lupus therapeutic area. • Write analytic code, build visualizations, and contribute to documentation and presentations for cross-functional stakeholders. Required Qualifications • 3–5 years of hands-on experience analyzing observational health data or real-world data (RWD). • Strong proficiency in R and SQL for data analysis and statistical modeling. • Experience with registry data and federated data networks. • Familiarity with standard data models such as the OMOP Common Data Model (CDM). • Proven ability to perform data quality assessments and generate insights from complex datasets. • Excellent written and verbal communication skills, especially with cross-functional and external collaborators. Preferred Qualifications • Experience with OHDSI tools and R packages (e.g., Atlas, Achilles, FeatureExtraction, CohortMethod). • Background in epidemiology, biostatistics, or health informatics.