Advanced Data Engineer
Novo Nordisk (company site, opens in a new tab)5+ yrsBengaluruOn-siteDirect career pageEst. 18–30 LPA
Posted 11h ago
Reach people at Novo Nordisk
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 Novo Nordisk 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
- Own production-grade data pipelines and products end to end, from design and deployment to day-to-day operation, supporting manufacturing and supply chain use cases.
- Build trusted, AI-ready datasets and data models; improve pipeline performance, reliability, cost, quality, monitoring and security.
- Investigate incidents and recurring defects, prevent repeat issues, and contribute reusable engineering standards and code reviews across a global team.
Minimum Requirements
- Work Experience Required: Typically 5+ years in data engineering or software engineering, with proven ownership of production-grade data solutions.
- Hands-on SQL, Python, Spark, data modelling, distributed data processing and Azure Databricks experience, including notebooks, orchestration/Workflows, Delta Lake and Unity Catalog.
- Experience with CI/CD, automated testing and infrastructure as code; working knowledge of data quality, observability, metadata, lineage, access controls and secure engineering.
- Bachelor’s degree in computer science, engineering, information technology or a related field, or equivalent relevant experience.
Ideal Candidate Profile
- Has owned production data solutions through deployment and ongoing operations, and can optimise them for performance, reliability and cost within architecture and platform guardrails.
- Can translate business and product needs into maintainable engineering solutions and embed testing, monitoring, governance and security into delivery.
- Supply chain experience in planning, logistics or warehousing is a useful advantage; experience with enterprise manufacturing or supply chain data is relevant to the team’s domain.