Senior Associate, Data Engineering & Semantics

We are seeking a Senior Associate to join our Commercial Excellence & Innovation (CEI) - Digital & Technology (Data Engineering & Semantics) team.

The Senior Associate is a hands-on specialist within Data Engineering & Semantics, executing assigned work within capability squads and CEI PODs across data engineering, data products, technical semantics and data quality.

Key Accountabilities:

• Deliver assigned data engineering, data product or data-quality work to agreed acceptance criteria and quality standards.
• Contribute hands-on build, testing, documentation, monitoring or support for Data Engineering across L1–L4 , ingestion, integration, transformation, storage and serving for structured, unstructured and knowledge data, and reporting and business intelligence foundations.
• Contribute hands-on build, testing, documentation, monitoring or support for Data Product Management , reusable, governed data products and data agents, including the CL layer of the AI Empowered Consumption Layer (AICL) and agent-facing data contracts.
• Contribute hands-on build, testing, documentation, monitoring or support for Technical Semantics , semantic models, metadata, taxonomies and technical mappings that implement DSM-approved business definitions and harmonized KPIs.
• Contribute hands-on build, testing, documentation, monitoring or support for Data Trust & Reliability Engineering , technical data governance controls, shift-left data quality checks, lineage, observability and China data, privacy and cross-border controls.
• Use approved AI-assisted engineering and analysis tools with rigorous human verification.
• Create technical specifications, data mappings, test evidence and reusable knowledge assets, and escalate blockers promptly.

Required Capabilities & Competencies:

• Hands-on data engineering , ingestion, transformation, SQL and Python, data modeling and reporting and business intelligence foundations.
• Working knowledge of semantic models, metadata, data products, data quality, lineage and data access for AI retrieval.
• Documentation, observability and lifecycle basics, applying privacy and security practices.
• Hands-on POD delivery and collaboration with GCA DSM, AITO engineers, production engineering, PIC and partners.
• Reliable, detail-oriented delivery with clear documentation and a collaborative, learning mindset.
• Bachelor’s degree or above in Computer Science, Data Engineering, Data Science, Information Systems or a related computing discipline.
• 5+ years of progressive relevant experience based on a bachelor’s degree, where a master’s or doctoral degree may reduce the years required.
• Experience in the pharmaceutical or another highly regulated industry, and the China technology, data, privacy or vendor environment, is preferred.

Measures of Success:

• Timely, high-quality delivery of assigned data engineering and data product tasks.
• Reliable, well-tested and well-documented pipelines and data products.
• Effective POD contribution with dependable transition to production.
• Reusable knowledge assets that improve quality, reuse and team efficiency.