Principal Data Engineer (AWS, Databricks, Ai, ML Flow, Data Architecture, Apache Airflow)
Mastercard (company site, opens in a new tab)12–18 yrsPuneOn-siteDirect career pageEst. 50–90 LPA
Posted 23d ago
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Job description
Structured overview of role & requirements
About This Role
- Lead design and engineering of scalable batch, streaming, and event-driven data platforms supporting analytics, ML, and AI workloads.
- Develop and operate governed, cloud-native lakehouse and modern data platform architectures with robust data pipelines and governance.
- Provide hands-on technical leadership including architecture reviews, mentoring, and collaborate across teams to mature R&D initiatives into production solutions.
Minimum Requirements
- 12-18 years relevant experience with significant ownership of complex enterprise data engineering solutions.
- Expertise in Python, PySpark, advanced SQL, Apache Spark, distributed data processing, and modern lakehouse platforms.
- Experience building and operating cloud-native data platforms on AWS, Azure, or similar; skilled in workflow orchestration tools like Apache Airflow or Databricks Workflows.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related technical field or equivalent experience.
Ideal Candidate Profile
- Strong hands-on technical leader comfortable turning ambiguous R&D objectives into secure, scalable production systems in innovation-focused environments.
- Experienced with modern data architectures (data lakes, lakehouses, data meshes, event-driven) and operational excellence including CI/CD, testing, and platform automation.
- Able to influence cross-functional teams without formal authority, balancing security, governance, performance, and cost in architectural decisions.