ApplySarthi

Staff, Data Engineer

Gsk

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What data roles keep asking for: Python (38%), SQL (33%), AWS (30%), Machine learning (19%), Spark (15%), Databricks (14%), Azure (13%), Stakeholder management (13%) — counted across their open postings here.

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Questions you are likely to be asked

  1. Why do you want to join Gsk?
  2. What is your experience with Databricks? Tell me one thing you learned the hard way.
  3. Walk me through a model you built, from the data to how it was used.
  4. How did you know your model was actually good, and not just good on your test set?
  5. Tell me about a time the data was messy or wrong. What did you do?

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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 Artificial Intelligence (AI), Data Analysis, Data Architecture Development, Database Engineering, Databricks Unified Data Analytics Platform, Data Engineering, Data Transformation, ETL Development, Machine Learning (ML), Python (Programming Language), Structured Query Language (SQL) 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. Important notice to Employment businesses/ Agencies GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site. It has come to our attention that the names of GlaxoSmithKline or GSK or our group companies are being used in connection with bogus job advertisements or through unsolicited emails asking candidates to make some payments for recruitment opportunities and interview. Please be advised that such advertisements and emails are not connected with the GlaxoSmithKline group in any way. GlaxoSmithKline does not charge any fee whatsoever for recruitment process. Please do not make payments to any individuals / entities in connection with recruitment with any GlaxoSmithKline (or GSK) group company at any worldwide location. Even if they claim that the money is refundable. If you come across unsolicited email from email addresses not ending in gsk.com or job advertisements which state that you should contact an email address that does not end in “gsk.com”, you should disregard the same and inform us by emailing askus@gsk.com , so that we can confirm to you if the job is genuine.

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Listed on workday · posted 2026-10-27. ApplySarthi collects openings and links to application pages; the role is advertised by Gsk, not by us.