Principal Data Engineer

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)