Data & Analytics Engineer – Commercial Pharma

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data & Analytics Engineer – Commercial Pharma based in Brazil.** We are seeking a Data & Analytics Engineer to build reliable data foundations supporting commercial pharmaceutical analytics. The role focuses on integrating complex datasets across sales, field activity, patients, market access, and omnichannel engagement. You will design scalable data models, develop robust ETL/ELT pipelines, and create curated datasets for analytical use. Your work will help standardize commercial metrics and enable more consistent, actionable insights across the organization. You will work with large and diverse healthcare datasets, including prescription, claims, specialty pharmacy, CRM, and digital data. The position combines advanced data engineering with strong knowledge of commercial pharma processes and analytics. This is an opportunity to contribute to high-impact data products that support commercial decision-making and field effectiveness. ### Accountabilities: - Design scalable data models covering field, brand, patient, market access, and omnichannel analytics. - Integrate and transform data from prescription and sales, claims, specialty pharmacy, patient hub, CRM, and digital sources. - Develop robust ETL/ELT pipelines and reusable, curated datasets for downstream analytics and reporting. - Build analytical marts, semantic layers, and standardized KPI definitions to support consistent commercial reporting. - Design fact and dimension models, including star schemas and conformed dimensions, for large-scale analytical environments. - Resolve and maintain accurate mappings across HCPs, HCOs, products, payers, geographies, and territories. - Perform HCP/HCO affiliation and NPI matching to improve data completeness and entity resolution. - Implement data quality controls, source-to-target validation, reconciliation processes, and automated data checks. - Use SQL and Python to process, validate, transform, and automate workflows involving large commercial datasets. - Collaborate with analytics, commercial, and business stakeholders to understand data requirements and deliver reliable, reusable data solutions. **Requirements:** - 5–8+ years of experience in analytics, data engineering, or a closely related field. - 3+ years of experience working with commercial pharmaceutical or biotechnology data is preferred. - Strong understanding of commercial pharma analytics, including sales and field effectiveness, patient analytics, and market access. - Advanced SQL skills with experience working with large and complex commercial datasets. - Strong Python skills for data processing, validation, automation, and workflow development. - Proven experience developing ETL/ELT pipelines using dbt or similar data transformation technologies. - Hands-on experience with Snowflake, Databricks, or comparable modern data platforms. - Strong expertise in dimensional data modeling, including fact and dimension design, star schemas, conformed dimensions, analytical marts, and semantic layers. - Experience with HCP/HCO affiliation, NPI matching, territory alignment, and commercial data mapping. - Familiarity with IQVIA Xponent and PlanTrak Rx datasets. - Experience working with specialty pharmacy and patient hub data feeds. - Knowledge of CRM data and platforms such as Veeva or Salesforce. - Strong analytical, problem-solving, communication, and stakeholder collaboration skills. - Ability to work independently while maintaining high standards for data quality, scalability, and maintainability. **Benefits:** - Full-time opportunity within a technology-focused environment. - Opportunity to work on complex commercial pharmaceutical data and analytics initiatives. - Exposure to diverse healthcare data sources, including sales, prescription, claims, specialty pharmacy, patient hub, CRM, and digital data. - Opportunity to work with modern data platforms such as Snowflake and Databricks. - Hands-on experience with advanced data engineering, modeling, ETL/ELT, and analytics technologies. - Opportunity to contribute to data products that support commercial decision-making and field effectiveness. - Collaborative environment involving data, analytics, commercial, and business stakeholders.