Staff, Data Engineer
GSK is hiring a Staff, Data Engineer. Level rates it ; you can apply on GSK.
Level's read on this job
- Level rates this data engineer role Works on AI ●●●○: the daily work is on AI products, without building the model.
- Tools and skills the posting cites include Databricks and AI agents.
- Level classifies it as a lead role.
- Level lists 839 other open roles at GSK.
- The role was posted in the last few days.
AI in this role
Job Purpose :
- Enterprise AI (EAI) builds the data foundations that GSK's AI systems depend on. In our operating model, digital workers do the hands-on data engineering. They generate pipelines, test and deploy them, monitor them in production and remediate failures. Our engineers design the system those digital workers operate in.
- At the centre of that system is a governed context layer. It makes data, meaning, permissions and history retrievable at the moment they are needed, and sits above our data platforms and beneath every experience and agent built on them.
- The Staff Data Engineer is accountable for implementing and evolving the governed context layer that humans and digital workers use across EAI, and is EAI's internal authority on data architecture for AI.
Responsibilities:
- Implement and evolve the governed context layer as a product: one layer across enterprise knowledge, structured data, business vocabulary and modelled ontologies, queried by humans and digital workers at run time.
- Build the capabilities the layer depends on: reliable cataloguing across structured and unstructured data, AI-assisted metadata enrichment, context design for each use case, secure agent access over standard protocols such as MCP, and routing across sources.
- Make certified meaning machine-readable so that every number a digital worker returns resolves to one governed definition, with clear ownership and lineage.
- Define the architecture standards and shared frameworks that digital workers use to generate, test, deploy and monitor pipelines, reducing the integration cost of each new AI initiative.
- Set the operating model for agent-generated engineering work: what digital workers do autonomously, what requires human approval, how quality is evaluated and how incidents escalate. Lead the incidents digital workers cannot resolve, from detection to permanent resolution.
Required skills:
- 10+ years in data engineering, including substantial time defining data architecture for production AI or ML systems at significant scale or complexity.
- Deep, hands-on experience building AI-grade data infrastructure: feature stores, vector databases, retrieval architectures, streaming data flows and evaluation datasets.
- Experience building context, metadata or semantic infrastructure that AI systems consume, such as catalogues, business glossaries, knowledge graphs or ontologies, exposed to agents over protocols such as MCP.
- Experience designing and operating AI agents or LLM-powered systems inside data workflows, including evaluation, guardrails and human-in-the-loop controls.
- A track record of defining data quality, lineage and governance standards for AI pipelines, including privacy, consent, PII handling and auditability, adopted across multiple data domains.
- Expert command of cloud data platforms and infrastructure as code, and a record of setting engineering standards that others adopted
Education:
- Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.
Preferred skills:
- Prior Staff, Principal or equivalent experience in an organisation operating a non-trivial data platform for AI.
- Experience in a regulated industry, such as life sciences, where data integrity and auditability are mandatory.
- Public technical contributions, such as talks, writing or open source, on data architecture for AI, context engineering or agent-operated data engineering.
#AGTech
Skills
Why GSK?
Uniting science, technology and talent to get ahead of disease together.
GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.
People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.
Inclusion at GSK:
As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.
Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs.
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How we rate this
Staff, Data Engineer at GSK rates 68 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical data engineer job
- $150k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through how you've used Databricks in your day-to-day work.
- Describe a typical day in a role like this one: which parts run through AI directly?
- If you removed AI from this role, what would be left, and how do you decide what still needs a human?
Adapt your resume
- List these exact terms on your resume: AI agents and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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