Design, develop, and maintain scalable ETL/ELT pipelines that support cross-functional data needs
Build, deploy, and optimize analytical dashboards and other reporting tools to support strategic decision-making
Write well-structured, testable code in Python, R, and SQL to support analytics workflows
Lead the development and best practices of version control, documentation, and coding standards
Partner with data scientists to bring analytics products from prototype to production
Identify and integrate AI-driven tools to enhance data processing and reporting capabilities
Mentor junior team members and contribute to a culture of knowledge sharing and continuous improvement

Bachelor’s degree required; Master’s degree preferred in a technical field such as Computer Science, Engineering, Data Science, or related disciplines
7+ years post-internship hands-on experience in a data engineering, analytics engineering, or data science role in the pharmaceutical industry
Proficient in Python, R, SQL, with experience operationalizing analytics at scale
Strong experience with Git and collaborative development workflows
Experience designing data architecture and maintaining complex pipelines
Familiarity with Databricks and other cloud platforms (e.g., Snowflake, AWS, Azure)
Experience in applying AI to streamline or augment data workflows
Excellent communication skills with a demonstrated ability to work across technical and non-technical teams
Experience working with patient-level data or in regulated environments (e.g., HIPAA, GxP, FDA) preferred

Data and Analytics Engineer III

Key skills