Data & Analytics Engineer – Commercial Pharma

Jobgether; United States; On-site; USD 110,000 - 170,000; Full time

Benefits offered by this job

Full-time role

Snowflake & Databricks exposure

Diverse healthcare data sources

Job summary

Jobgether partners with a pharma-focused company to seek a Data & Analytics Engineer in the United States. You will build reliable data foundations to support commercial pharmaceutical analytics and integrate data across sales, field activity, patients, market access, and omnichannel engagement.

Design scalable data models, develop ETL/ELT pipelines, and create curated datasets for analytics and reporting. You will enable standardized KPIs and actionable insights across the organization while

Qualifications

• 5–8 years of analytics or data engineering experience.
• 3 years working with commercial pharmaceutical or biotechnology data preferred.
• Strong understanding of commercial pharma analytics including sales, field effectiveness, patient analytics, and market access.
• Advanced SQL skills with large, complex datasets.
• Strong Python skills for data processing, validation, automation, and workflow development.
• Experience developing ETL/ELT pipelines using dbt or similar tools.
• Hands-on experience with Snowflake, Databricks, or comparable platforms.
• Expertise in dimensional data modeling, including star schemas and semantic layers.
• Experience with HCP/HCO affiliation, NPI matching, and data mapping.
• Familiarity with IQVIA Xponent and PlanTrak Rx datasets.
• CRM data knowledge with 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 ETL/ELT pipelines and curated datasets for downstream analytics and reporting.
• Build analytical marts, semantic layers, and standardized KPI definitions for reporting.
• Design fact and dimension models, including star schemas and conformed dimensions for large-scale analytics.
• Resolve and maintain mappings across HCPs, HCOs, products, payers, geographies, and territories.
• Perform HCP/HCO affiliation and NPI matching to improve data quality.
• Implement data quality controls, source-to-target validation, and automated checks.
• Use SQL and Python to process, validate, transform, and automate workflows for large datasets.
• Collaborate with analytics, commercial, and business stakeholders to deliver reliable data solutions.

Skills

SQL

Python

ETL/ELT

Data modeling

Stakeholder collaboration

Problem solving

Independent work

Tools

dbt

Snowflake

Databricks

Salesforce

Veeva

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 United States.

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 conferred 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, conferred 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.