Data & Analytics Engineer – Commercial Pharma
New
J
JobgetherPharmaceutical Data
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5–8+ years
- Required Skills
- PythonSQLSalesforceSnowflakedbtDatabricks
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.
Responsibilities
- 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.
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