Senior Principal Data Engineer

BeiGene

1d•$150,900 - $195,900

About The Position

BeOne continues to grow at a rapid pace with challenging and exciting opportunities for experienced professionals. When considering candidates, we look for scientific and business professionals who are highly motivated, collaborative, and most importantly, share our passionate interest in fighting cancer. We are seeking a Senior Principal Data Engineer to serve as one of the highest-level technical leaders within the Data Engineering organization. Responsible for defining technical strategy, architecting enterprise-scale data platforms, and delivering scalable, secure, and high-quality data solutions. This role combines deep expertise in cloud data platforms, software engineering, enterprise data architecture, data modeling, AI-assisted development, and technical governance. The successful candidate will influence technical direction across multiple business domains, establish engineering standards, and mentor engineers while remaining hands-on with architecture and solution design.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field (Master's preferred).
7+ years of experience in Data Engineering, Software Engineering, or Data Platform Engineering.
Extensive experience designing enterprise-scale cloud data platforms and data architectures.
Strong expertise in conceptual, logical, and physical data modeling.
Expert-level programming skills in Python and SQL.
Hands-on experience with Databricks, Azure, AWS, Docker, Kubernetes, Git, REST APIs, and CI/CD.
Strong understanding of software architecture, design patterns, distributed systems, and secure data platform design.
Demonstrated ability to lead highly complex technical initiatives through influence rather than direct people management.
Excellent communication, stakeholder management, and technical leadership skills.

Nice To Haves

Experience in pharmaceutical, biotechnology, healthcare, or other regulated industries.
Experience with Databricks Unity Catalog, enterprise data governance, metadata management, and observability.
Experience with Infrastructure as Code (Terraform, Bicep, or similar).
Experience applying Generative AI to software and data engineering.

Responsibilities

Design and evolve enterprise-scale data platform architecture supporting analytical, operational, and AI workloads.
Define reusable engineering frameworks, reference architectures, and technical standards.
Drive long-term platform strategy focused on scalability, security, performance, and maintainability.
Lead architecture reviews, technical design, and engineering governance for strategic initiatives.
Partner with engineering leadership to define technology roadmaps and drive engineering best practices.
Influence technical decisions across multiple teams without direct people management.
Design scalable ETL/ELT frameworks, enterprise data pipelines, and logical and physical data models.
Develop reusable, metadata-driven data integration frameworks.
Establish standards for data modeling, metadata management, lineage, and performance optimization.
Champion AI-assisted software development across the engineering organization.
Evaluate and implement AI coding assistants and engineering productivity tools.
Build intelligent engineering accelerators, including: AI-assisted code generation, Automated documentation, Code review assistance, Test generation, Data quality validation, AI-powered engineering copilots.
Drive engineering productivity and innovation through Generative AI.
Provide technical leadership across modern cloud technologies including: Databricks, Azure, AWS, Docker, Kubernetes, Python, SQL, Git, REST APIs, CI/CD.
Design secure data platforms with enterprise governance, metadata, lineage, and RBAC/ABAC access controls.
Establish software engineering best practices including code quality, testing, automation, observability, and reusable components.
Mentor senior engineers and principal engineers while fostering engineering excellence.
Partner with Product Management, Data Science, Analytics, Data Governance, Enterprise Architecture, and Infrastructure teams to deliver strategic data capabilities.