Data Architect II

Overview

The Onyx Research Data Tech organization is GSK’s Research data ecosystem which has the capability to bring together, analyze, and power the exploration of data  partner with scientists across GSK to define and understand their challenges and develop tailored solutions that meet their needs. The goal is to ensure scientists have the right data and insights when they need it to give them a better starting point for and accelerate medical discovery.

Ultimately, this helps us get ahead of disease in more predictive and powerful ways.

Onyx is a full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and Dev Ops, data / metadata / knowledge platforms, and AI/ML and analysis platforms, all geared toward:

• Building a next-generation, metadata- and automation-driven data experience for GSK’s scientists, engineers, and decision-makers, increasing productivity and reducing time spent on “data mechanics”

• Providing best-in-class AI/ML and data analysis environments to accelerate our predictive capabilities and attract top-tier talent

• Aggressively engineering our data at scale, as one unified asset, to unlock the value of our unique collection of data and predictions in real-time

The Onyx Data Architecture team sits within the Data Engineering team, which is responsible for the design, delivery, support, and maintenance of industrialized automated end to end data services and pipelines. They apply standardized data models and mapping to ensure data is accessible for end users in end-to-end user tools through use of APIs. They define and embed best practices and ensure compliance with Quality Management practices and alignment to automated data governance.

They also acquire and process internal and external, structure and unstructured data in line with Product requirements.

As a Data Architect II, you ll apply your expertise in big data and AI/GenAI workflows to support GSK s complex, regulated R&D environment.

You ll contribute to designing Data Mesh/Data Fabric architectures while enabling modern AI and machine learning capabilities across our platform.

• Partner with the Scientific Knowledge Engineering team to develop physical data models to build fit-for-purpose data products

• Design data architecture aligned with enterprise-wide standards to promote interoperability

• Collaborate with the platform teams and data engineers to maintain architecture principles, standards, and guidelines

• Design data foundations that support GenAI workflows including RAG (Retrieval-Augmented Generation), vector databases, and embedding pipelines

• Work across business areas and stakeholders to ensure consistent implementation of architecture standards

• Lead reviews and maintain architecture documentation and best practices for Onyx and our stakeholders

• Adopt security-first design with robust authentication and resilient connectivity

• Provide best practices and leadership, subject matter, and GSK expertise to architecture and engineering teams composed of GSK FTEs, strategic partners, and software vendors.

• Bachelor’s degree in computer science, engineering, Data Science or similar discipline

• 5+ years of experience in data architecture, data engineering, or related fields in pharma, healthcare, or life sciences R&D

• 3+ years’ experience of defining architecture standards, patterns on Big Data platforms

• 3+ years’ experience with data warehouse, data lake, and enterprise big data platforms

• 3+ years’ experience with enterprise cloud data architecture (preferably Azure or GCP) and delivering solutions at scale

• 3+ years of hands-on relational, dimensional, and/or analytic experience (using RDBMS, dimensional, No
  
  SQL data platform technologies, and ETL and data ingestion protocols)

• Master's or PhD in computer science, engineering, Data Science or similar discipline

• Deep knowledge and use of at least one common programming language: e.g., Python, Scala, Java

• Experience with AI/ML data workflows: feature stores, vector databases, embedding pipelines, model serving architectures

• Familiarity with GenAI/LLM data patterns: RAG architectures, prompt engineering…