Senior Software Engineer, Data & AI Platform: Reusable Metrics

Mastercard Inc.

Arlington (VA)

Sur place

USD 132 000 - 212 000

Plein temps

14 jours+

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Résumé du poste

Mastercard is seeking a Senior Software Engineer for the Data Foundations team in the US. You will design, build, test, and release shared services and pipelines that enable analytics products across Mastercard's data platforms.

The role emphasizes collaboration with product managers, establishing reusable data assets, and ensuring secure, observable production systems. You will prototype data access controls, versioned metrics, and enterprise-grade abstractions while shaping engineering

Qualifications

  • Strong expertise in software design, testing, CI/CD, and operating production services.
  • Experience designing APIs, reusable libraries, and platform capabilities consumed by other teams.
  • Solid understanding of data modeling, entity relationships, and data contracts.

Responsabilités

  • Design, build, test, and release shared services, libraries, and pipelines that enable and support other engineering teams.
  • Partner with product managers to understand business problems, challenge assumptions, and simplify solutions.
  • Create clear design proposals outlining trade-offs and implementation approaches.
  • Develop reusable, versioned interfaces with comprehensive documentation and automated deployment and testing capabilities.

Connaissances

Advanced SQL
Python/Java/C#/Go/TS
Cloud data platforms
API design
Data modeling

Outils

Databricks
Snowflake
AWS
Azure
Google Cloud

Description du poste

Mastercard is seeking a Senior Software Engineer for the Data Foundations team in the US. You will design, build, test, and release shared services and pipelines that enable analytics products across Mastercard's data platforms.

The role emphasizes collaboration with product managers, establishing reusable data assets, and ensuring secure, observable production systems. You will prototype data access controls, versioned metrics, and enterprise-grade abstractions while shaping engineering

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