Process Data Engineer I - Pharmaceutical Product Development

Role Overview

At Bristol Myers Squibb, we are seeking an early-career Data Engineer passionate about creating high-quality scientific data products and contributing to the development of data fabric to support advanced analytics, AI, machine learning, and scientific decision-making across Pharmaceutical Product Development. The ideal candidate will be an engineer who sees data as a strategic asset that powers scientific discovery and AI innovation, working on complex datasets and modern cloud technologies.

Responsibilities

• Build scientific data products for product development focused on datasets for molecular features, material properties, laboratory data, process and manufacturing parameters, stability and product performance data.
• Contribute to initiatives for data structuring and data contextualization.
• Develop and maintain scalable data pipelines supporting analytics, AI, and scientific modelling initiatives.
• Transform raw scientific and operational data into trusted, model-ready data products.
• Work with cross-functional teams to improve data accessibility, reliability, and quality.
• Support enterprise initiatives involving Databricks, Data Fabric, and cloud-native architectures.
• Automate data workflows and reduce manual effort through engineering best practices.
• Contribute to development of reusable data assets supporting Product Development innovation across US, Europe and India.

Requirements

• Bachelor's or Master’s degree in Computer Science, Chemical Engineering, Information Systems, Bioinformatics, Biotechnology or related field with 2+ years of industry experience.
• Technical hands-on experience with modern data technologies.
• Proficiency in SQL, Python, ETL/ELT Development, Delta Lake, Lakehouse Architecture, Medallion Architecture, Data Modelling, Data Warehousing, Distributed Computing, and Data Validation.
• Experience with Databricks, dbt, and AWS.
• Knowledge of Data Product Design, Vector Databases, Data Quality Engineering, Data Observability, Metadata Management, Master Data Management, Data Lineage, and Data Governance principles.
• Familiarity with AI-Ready Data Foundations, Vector Database fundamentals, Semantic Layer Design, Knowledge Graph Concepts, and Data Foundations for GenAI & Agentic AI Applications.
• Software Engineering & Delivery Practices including Git, API Integration, Workflow Automation, CI/CD Fundamentals, and Agile Delivery.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.

Skills

• Python
• SQL
• AWS
• Databricks
• dbt