Mastercard is seeking a Principal Data Platform Architect to shape and deliver the next generation of enterprise data capabilities. This pivotal role will lead the design and evolution of strategic platforms, simplifying the secure exchange of external data with Mastercard. The objective is to empower product teams, consultants, and analysts with easier discovery, access, and utilization of enterprise data. This position aims to transform complex, fragmented data processes into scalable, self-service experiences, fostering innovation and maximizing the value derived from Mastercard's data assets.

Drive the technical vision and architecture for enterprise data onboarding, exchange, discovery, and consumption. Develop strategic roadmaps balancing immediate business needs with long-term platform scalability. Identify opportunities to simplify and standardize recurring data workflows. Design capabilities for streamlined, secure external data onboarding, sharing, and management. Establish reusable patterns for data integration, governance, observability, lineage, and compliance. Reduce reliance on bespoke solutions through automation and self-service.

Create intuitive ways for teams to discover and utilize trusted enterprise data. Enable self-service analytics and rapid solution development. Partner with governance and security teams for balanced accessibility and control. Serve as a technical authority across engineering organizations, influencing architecture and technology decisions. Establish engineering standards, best practices, and reference architectures. Mentor engineers and architects. Collaborate with Product, Engineering, Data Governance, Security, and business stakeholders to solve complex data challenges. Translate business needs into scalable technical solutions. Build alignment around platform investments and strategic priorities.

Extensive experience architecting and building large-scale data platforms, distributed systems, and enterprise integration solutions across on-premises and cloud environments. Proficiency with technologies such as Spark, Kafka, Flink, NiFi, Hadoop/Cloudera, Databricks, and modern cloud-native data services. Deep expertise in data engineering, data architecture, data integration, and cloud-native platforms. Experience building reusable platforms serving multiple products, teams, or business domains. Strong understanding of data governance, privacy, security, metadata, and data quality practices.

Demonstrated ability to influence technical direction across organizations. Exceptional communication skills with both technical and non-technical stakeholders. Proven track record of translating ambiguous business challenges into scalable technical solutions. Proficiency in Python, SQL, and data ecosystems including Oracle, AWS Glue, Azure Data Factory, BigQuery, and Snowflake.