Python Data Engineer
Kyndryl (company site, opens in a new tab)4–8 yrsBengaluruOn-siteDirect career pageEst. 14–26 LPA
Posted 10h ago
Reach people at Kyndryl
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 Kyndryl 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
- Design, develop, and optimize scalable data pipelines and ETL/ELT solutions using Databricks, PySpark, Python, SQL, Delta Lake, and Medallion Architecture.
- Build and maintain Python-based REST APIs and backend services (FastAPI) for secure data ingestion, integration, and consumption across platforms.
- Deploy cloud-native data engineering solutions on Microsoft Azure, ensuring engineering excellence through automated testing, monitoring, and performance tuning.
Minimum Requirements
- 4–8 years of experience in Data Engineering, Python Development, or related engineering roles.
- Strong proficiency in Python including OOP, API development, exception handling, and asynchronous programming.
- Hands-on experience with Databricks, PySpark, SQL, ETL/ELT pipelines, and REST API development.
- Experience with Microsoft Azure cloud platform including Azure Databricks, Data Lake Storage, and CI/CD pipelines.
Ideal Candidate Profile
- Experienced in building scalable, secure, and production-ready data engineering solutions in a cloud environment using modern Lakehouse architectures.
- Comfortable collaborating with architects, platform engineers, DevOps teams, and stakeholders to deliver enterprise-grade data platforms.
- Strong engineering mindset focused on scalability, automation, code quality, performance optimization, and operational excellence.