Principal Data Engineer
GSK (company site, opens in a new tab)7+ yrsBengaluruOn-siteDirect career pageEst. 28–45 LPA
Posted 15h ago
Reach people at GSK
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at GSK you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead technical strategy and implementation for data and integration flows supporting digital product profiles across R&D, CMC, and commercial systems.
- Design, deploy and maintain CI/CD pipelines, data pipelines, and integration services ensuring quality, security, and compliance with validation and change management requirements.
- Collaborate with ontology and AI teams to integrate ontologies, knowledge graphs, and AI-enabled capabilities, driving improved data reuse, quality, and faster access.
Minimum Requirements
- Bachelor's or master's degree in computer science, IT, software engineering, or related field, or equivalent experience.
- Minimum 7 years of experience in data engineering, integration engineering, DevOps or similar roles within complex enterprise or manufacturing environments.
- Demonstrated experience designing data/integration solutions across R&D, CMC and/or manufacturing domains; familiarity with PDLM or equivalent digital product profile platforms.
- Experience with ontologies, knowledge graphs, AI/ML integration, and systems such as MES, LES/LIMS, ERP, or regulatory platforms.
Ideal Candidate Profile
- Experienced in bridging product data flow from research through manufacturing to commercial release in regulated industries, applying technical and domain expertise.
- Comfortable working cross-functionally with R&D, DevOps, AI, and ontology teams to operationalize complex data and integration architectures.
- Strong focus on compliance, validation, and responsible AI governance within deployment pipelines and engineering processes.