Back
Tier-1 brand, Pune metro, and broad data engineering skillset drive high competition.
Requires specialized data-platform expertise and payments familiarity, so moderate transferability across industries.
Multiple mandatory platforms, tooling, and senior leadership expectations indicate high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design and build of scalable data pipelines and storage schemas across Mastercard's cloud and on-premise ecosystems.
Define and promote data engineering best practices, reference architectures, and engineering standards for data ingestion, transformation, storage, and consumption.
Collaborate with stakeholders to translate data requirements into scalable solutions ensuring enterprise governance, security, privacy, and compliance adherence.
Proven experience designing and building end-to-end data pipelines using ETL/ELT patterns.
Expert proficiency in Python, Java, or equivalent programming languages and advanced SQL including performance tuning for large datasets.
Deep hands-on experience with distributed data processing frameworks (e.g., Apache Spark) and platforms like Azure Data Fabric and Databricks.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced data engineer familiar with data lake, lake house, and data warehouse architectures with strong data modeling skills.
Able to operate on a global team, capable of communicating plans and leading execution across time zones.
Experience or background in financial services, payments, or other high-scale, high-reliability domains preferred.