Mastercard is a global leader in empowering economies and people across 200+ countries. We drive a sustainable economy through secure, simple, and smart digital payment choices. Our innovative technology, strong partnerships, and extensive networks deliver unique products and services that unlock potential for individuals, businesses, and governments.

We are seeking a Principal Software Engineer, Data Architecture to lead our global enterprise data architecture strategy. This senior technical role is crucial for defining how data is architected, governed, secured, and utilized across Mastercard's intricate global systems. You will establish the architectural vision for modernizing our data platforms in hybrid cloud and on-premises settings, supporting both high-volume batch and low-latency real-time data processing. This position demands deep technical expertise coupled with influential leadership to maximize data asset value while adhering to the highest standards of security, resilience, compliance, and operational excellence.

As the senior technical leader for enterprise data architecture, you will partner closely with the VP of Data Engineering and senior technology leadership.

You will serve as a trusted advisor to executive stakeholders, translating business strategies into scalable, secure, and resilient data architecture decisions.

Key responsibilities include defining and evolving Mastercard's global data architecture strategy across hybrid cloud and on-premises environments. You will architect secure, resilient solutions that comply with global regulations such as GDPR and ISO 20022, including data localization requirements.

Championing Data Mesh principles, you will enable data-as-a-product capabilities, federated ownership, governance, and self-service access. You will also establish enterprise architecture standards and best practices through the Data & Analytics Architecture Review Board, leading the modernization of legacy data platforms to cloud-native architectures on AWS, Azure, and GCP.

Drive the adoption of modern data technologies like Databricks, Snowflake, Delta Lake, and streaming platforms, enabling both real-time and batch analytics for critical workloads. Evaluate and integrate emerging technologies, including AI-enabled data platforms and agent-based architectures.

Furthermore, you will mentor and influence global engineering teams, fostering a culture of technical excellence, accountability, and thoughtful risk-taking.

We are looking for candidates with proven experience as a Principal Engineer, Lead Architect, or in an equivalent technical leadership role focused on enterprise-scale data architecture and platform strategy.

A deep expertise in designing, building, and operating distributed data systems at a global scale is essential. Strong hands-on experience with technologies like Apache Spark, Kafka, Flink, NiFi, Databricks, Snowflake, and modern cloud-native data platforms is required.

Demonstrated success in modernizing data platforms using cloud-native architectures across AWS, Azure, and/or GCP is crucial. Experience integrating AI-driven capabilities into data platforms, with appropriate governance and guardrails for emerging use cases, is also a key requirement.

A solid understanding of data governance, security, and regulatory compliance in highly regulated, global environments is necessary. You must have a proven ability to lead and influence architectural initiatives within Agile, SAFe, or product-centric delivery models, collaborating effectively with product, engineering, and business stakeholders.

We seek individuals with the ability to influence technical and business decisions at all levels, including C-suite stakeholders, and possess strong executive presence to translate complex architecture concepts into business language. Exceptional communication skills are vital for articulating technical concepts to diverse audiences.

A strong Decency Quotient (DQ) and a track record of building inclusive, collaborative, high-performing teams are highly valued.

A Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent practical experience, is required.