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 at scale. We 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 DevOps, 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, structured and unstructured data in line with Product requirements.
This role is responsible for building and leading a scrum team of world-class data architects focused on creating the data ecosystem of Onyx. They support the head of Data Engineering in building a strong culture of accountability and ownership in their team, as well as developing innovative data solutions to ensure the data is integrated into our platforms and downstream applications. They work in close partnership with the rest of data engineering teams to ensure we are following the best engineering standards and practices, and with our customers and product teams to ensure the use of appropriate schemas, vocabularies, and ontologies.
Key Responsibilities
- Lead a team of data architects in delivering robust data and knowledge products that advance GSK R&D
- Experience influencing, motivating and aligning others towards common technical decisions
- Experience with modern data architecture approaches and data management capabilities such as metadata management, lineage, data governance, data discovery, and data quality
- Define and implement data modelling and design principles, ensuring data structures are optimized for performance, scalability, and data integrity
- Partner closely with other data engineering leads to conceptualize the design of new data flows aimed at maximizing reuse and aligning with an event-driven microservice enabled architecture
- Partner with data engineering leads, stakeholders and product team to develop data standards and best practices to deliver seamless data flow and interoperability across systems and applications
- Evaluate and select, deploy and use modern data architecture and modelling technologies that align with GSK’s data strategy & technology stack
- Exemplar and strategic leader that can envision the future of data architecture in GSK
Why You?Basic Qualifications:
We are looking for professionals with these required skills to achieve our goals:
- Bachelor’s degree in Data Engineering, Computer Science, Software Engineering or related field.
- 8+ years of relevant work experience
- 5+ years of people management experience
- Cloud experience (e.g., AWS, Google Cloud, Azure, Kubernetes)
- Experience in leading teams and fostering collaboration to deliver data architecture solutions
- Knowledge of Data architecture principles, data modelling techniques and data integration
Preferred Qualifications:
- Deep knowledge and use of at least one common programming language: e.g., Python, Scala, Java
- Expertise in data modelling, database concepts and SQL
- Experience with agile software development environments using tools like Jira and Confluence
- Strong understanding of data architecture principles and practices such as data as a product, Data as a Service, Data Mesh, Data Fabric, Data Virtualization, etc.
- Experience in data governance, data security and privacy relevant to the industry
- Experience with common big data tools (e.g., Spark, Kafka, Storm, …) a plus
J-18808-Ljbffr
