Process Data Engineer I - Pharmaceutical Product Development

Scientific Data Product Creation: Build high-quality scientific data products focusing on molecular features, material properties, laboratory datasets, chemical process/manufacturing parameters, stability, and product performance data.

Data Structuring & Contextualization: Transform raw operational and laboratory scientific data into structured, contextualized, and trusted model-ready assets to power AI, ML, and advanced analytics.

Pipeline Development & Maintenance: Design, deploy, and maintain scalable ETL/ELT data pipelines using modern Lakehouse and Medallion architectures (Databricks, Delta Lake, dbt, AWS).

Cross-Functional Collaboration: Partner with scientists, analysts, data scientists, and engineers to optimize data accessibility, reliability, lineage, metadata management, and data quality across US, Europe, and India regions.

Data Foundation for AI Innovation: Support enterprise Data Fabric initiatives, semantic layer design, knowledge graph concepts, vector databases, and foundational data structures for GenAI and Agentic AI applications.

Automation & Software Engineering: Automate data workflows, implement robust CI/CD, apply Git version control, and enforce modern data observability and software engineering best practices.

Data Engineering & Cloud Platforms: SQL, Python, PySpark, Delta Lake, Lakehouse/Medallion Architecture, Databricks, dbt, AWS.

AI & Modern Data Practices: Vector databases, semantic layers, data cataloging, metadata management, master data management, and CI/CD pipelines.

AI-Native Transformation: Direct contribution to building the foundational data architecture required to achieve an AI-native state in pharmaceutical product development.

Global Exposure: Collaborative working environment supporting cross-border pharmaceutical product development teams with comprehensive health, flexibility, and professional growth opportunities.