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GSK

Principal Data Engineer

GSK is hiring a Principal Data Engineer. Level rates it ; you can apply on GSK.

Level's read on this job

  • On Level's scale, this data engineer role is Little AI ●○○○: aI is not part of the work.
  • The posting mentions MLflow, Databricks and RAG among its tools and skills.
  • Level classes it as a lead role.
  • GSK has 947 other open roles on Level.
  • Posted in the last few days.

AI in this role

MLflowDatabricks
ragai-agentsml-ops

Job Purpose :


GSK is one of the world's preeminent pharmaceutical and healthcare companies. We take pride in leading a healthcare revolution. Through disruptive approaches in R&D and commercial business processes, our Data & Analytics team integrates, simplifies, and unlocks all our data. This drives innovation, decision-making, and allows us to better serve our patients, healthcare professionals, and consumers.

This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:


Responsibilities:


  • Build and maintain our data foundation: Design, build, and run scalable, production-grade data pipelines that collect, clean, transform, and deliver data. Make sound architectural choices and help set best practices for the team.
  • Create data products for analytics and AI: Build reusable datasets, feature tables, and, where needed, retrieval infrastructure (such as vector databases) that support dashboards, models, and AI applications. Define clear data contracts so every consumer gets consistent, quality-checked inputs.
  • Keep our data accurate and trustworthy: Implement data quality checks, validation rules, monitoring, and alerting. Track data lineage (where data comes from and where it's used) so we're always audit-ready.
  • Turn business questions into data solutions: Work with business partners, data scientists, and analysts to gather requirements, agree on delivery timelines and service levels, and deliver self-service data that answers real business questions.
  • Prepare data for modeling: Create, version, and maintain the training and evaluation datasets used by data scientists. Make sure results are reproducible and that data quality supports model performance.
  • Automate with AI tools/Agent : Build or use LLM-powered tools and agents to automate data checks, monitoring, and reporting. Set sensible guardrails and decide when human review is needed.
  • Fix problems quickly and permanently: Investigate pipeline and data incidents, find the root cause, and implement lasting fixes that improve reliability.
  • Share your knowledge: Set standards for code, documentation, and governance. Conduct code reviews and mentor junior and mid-level engineers.
  • Keep improving: Refine frameworks, tools, and automation to reduce manual effort, and stay current with emerging data and AI engineering practices.

Preferred Skills:


  • 5+ years of experience as a data engineer, including building and operating production data pipelines
  • Experience supporting data scientists or analysts and translating their needs into reliable datasets
  • Strong SQL (complex queries, performance tuning) and Python, including PySpark for large-scale data processing
  • Hands-on experience with Azure Databricks: Delta tables, Unity Catalog, Databricks Workflows, and SQL Warehouses
  • Experience supporting ML projects using Azure ML, MLflow, or Databricks (feature tables, MLOps, dataset versioning, vector search, RAG pipelines)
  • Experience building LLM-based automation or AI agents, or using Databricks Genie for self-service analytics


Required skills:


  • Experience designing ETL/ELT pipelines and orchestrating them with Azure Data Factory, Databricks Workflows
  • Experience with Git, Azure DevOps (or GitHub) CI/CD pipelines, and automated testing for data code
  • Experience implementing data quality checks, monitoring, and data cataloging or governance (for example Collibra or Unity Catalog)
  • Solid understanding of data modeling (dimensional models, data warehouses, lakehouse architecture)
  • Experience with Git, Azure DevOps (or GitHub) CI/CD pipelines, and automated testing for data code
  • Experience implementing data quality checks and monitoring
  • Working knowledge of Power BI or similar tools for serving data to business users



#AGTech




Skills

Azure Devops, Computer Programming, Data Analysis, Data Engineering, Data Governance, Data Ingestion, Data Integration, ETL Development, ETL Processing, MLflow, Performance Tuning, PySpark, 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.

How we rate this

Principal Data Engineer at GSK rates 4 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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

RAGAI agentsML OpsMLflowDatabricks

Also common in data engineer jobs at this level

Pay

Typical data engineer job
$147k

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

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. How do you monitor a model once it's live, and how do you know it needs retraining?
  4. What's a project where you used MLflow hands-on?
  5. Walk me through how you've used Databricks in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, ML Ops, MLflow, 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.

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