Principal Data Engineer (Data Platforms)

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Principal Data Engineer (Data Platforms) Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships, and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview The Mastercard Services Technology team is looking for a Principal Data Engineer to unlock the potential of our data assets by innovating continuously, removing friction from how large-scale data is stored, managed, governed, and accessed, and establishing standards across public-cloud and on-premises environments. We are looking for a hands-on, passionate engineer and architect with strong PySpark, Python, cloud and modern data-architecture expertise. This role will design scalable distributed and lakehouse platforms across regions and execution environments; establish federated governance, metadata, access, and query patterns; build reliable data solutions; shape engineering culture; and mentor others. It is a role for builders and collaborators who value clean pipelines, cloud-native design, sound architecture decisions, and helping teammates succeed. Role Design and build scalable, secure, cloud-native and hybrid data platforms and control-plane services using Java and/or Python, PySpark, REST and gRPC APIs, and modern data and software engineering practices, supporting business-critical batch, streaming, API, and secure data-sharing use cases. Architect large-scale distributed data and lakehouse platforms spanning regions, public clouds, on-premises, and private or sovereign execution environments. Define control-plane and data-plane responsibilities and clear boundaries among global governance, local enforcement, metadata and orchestration, and local workload execution. Design control-plane services and interfaces for platform configuration, metadata, policy, identity and access, provisioning, orchestration, lifecycle management, and observability using secure API-first and event-driven patterns. Design federated metadata, catalog, identity, access, query, and data-sharing patterns; evaluate federation, replication, and compute-to-data using latency, cost, regulation, residency, freshness, reliability, and operational complexity. Define data-product and data-contract standards covering ownership, interfaces, schema evolution, compatibility, lineage, quality, observability, and SLA/SLO commitments. Design processing and query architectures using Databricks, Snowflake, Spark, and Trino, with open table and file formats such as Iceberg, Delta Lake, and Parquet. Define catalog and metadata architectures using Unity Catalog, AWS Glue Data Catalog, and open catalog patterns such as Apache Polaris. Design AWS data-platform architecture using S3, IAM, EKS, networking, and relevant cloud-native data services, and Azure architecture using ADLS, Microsoft Entra ID, Azure RBAC, Key Vault, AKS, and relevant analytics services. Design Kubernetes platforms for portable compute, workload isola