Principal Data Engineer (Data Platforms)
Mastercard (company site, opens in a new tab)10+ yrsPuneOn-siteDirect career pageEst. 45–90 LPA
Posted on 15 Sept
Reach people at Mastercard
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 Mastercard 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 and build scalable, secure, cloud-native and hybrid data platforms and control-plane services supporting batch, streaming, API, and secure data-sharing use cases.
- Architect large-scale distributed and lakehouse platforms across multiple regions and clouds, establishing federated governance, metadata, access, and query patterns.
- Own infrastructure optimization and establish platforms' architecture standards, mentoring engineers and collaborating cross-functionally to influence technical direction.
Minimum Requirements
- 10+ years of hands-on data and software engineering experience with strong Java and/or Python, PySpark, API/backend service development skills.
- Experience designing large-scale distributed or lakehouse platforms across multiple regions, clouds, or execution environments.
- Strong cloud data-platform experience on AWS and/or Azure including key services like S3, IAM, Databricks, EKS, ADLS, AKS etc.
- Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent hands-on experience.
Ideal Candidate Profile
- Experienced architect and builder capable of defining control-plane and data-plane boundaries, governance, and federated metadata/query architectures.
- Strong expertise in modern data architectures including lakehouse, medallion, batch/streaming processing, and secure data sharing.
- Comfortable in hands-on coding, platform design, systems integration, and leading engineering standards and mentorship in a complex multi-cloud environment.