Global Payments Inc.

Head of Enterprise Data Engineering

Global Payments Inc.

full-time

Posted on:

Location: 🇺🇸 United States

Visit company website
✨ AI Apply
Apply

Job Level

Lead

Tech Stack

CloudKafka

About the role

  • Define and own the enterprise data engineering strategy and reference architecture for AI-ready data, including cloud platform, data products, and automation-first delivery model
  • Lead architectural decisions for lakehouse patterns, streaming, CDC, and event-driven integration
  • Architect, implement, and operate hybrid and cloud-native data platforms with heavy automation
  • Establish trusted domains focusing on security, governance, and reuse across business lines
  • Lead design and delivery of reusable, trusted data products with clear SLAs, documentation, versioning, and APIs; enforce data contracts between producers and consumers
  • Enable secure, governed data sharing and monetization where appropriate
  • Provide platform services and reusable capabilities for data science and AI: feature store, model-ready curated layers, governed sandboxes, MLOps integration, and model/data lineage
  • Embed data governance within pipelines: lineage capture, data classification, role-based and attribute-based access, fine-grained controls, and consent management
  • Implement DQ-by-design: thresholding, anomaly detection, reconciliation, and data SLAs enforced in CI/CD and runtime with automated quarantine/retry/escalation
  • Manage a multi-million-dollar budget and optimize build-vs-buy decisions, licensing, cloud spend, and vendor relationships
  • Oversee large-scale data migration, modernization, and platform implementation projects
  • Scale, mentor, and inspire a diverse, high-performing data engineering and architecture team; develop adaptive hiring and resourcing strategies
  • Ensure compliance with all risk, regulatory, and audit standards and maintain rigorous internal controls

Requirements

  • 15+ years in engineering and/or data and analytics
  • 8+ years leading large-scale data engineering and platform teams in complex, regulated environments
  • Deep expertise in data architecture and engineering: data modeling (OLTP/OLAP), big data and query engines, lakehouse, data warehousing, MDM, data integration, CDC, and large-scale batch/stream processing
  • Experience delivering data products at scale with embedded governance, metadata/lineage, and continuous DQ; strong background in data contracts and data observability
  • Real-time data streaming expertise (e.g., Kafka, Pub/Sub, Kinesis), event-driven architectures, and change data capture patterns
  • Proven success designing and operating enterprise cloud-native data platforms on at least one hyperscaler
  • Practical experience enabling AI/ML: feature stores, model-ready datasets, MLOps integration, and privacy-preserving patterns
  • Comfortable partnering with data scientists and ML engineers
  • Executive presence with ability to translate complex architectures into business value and present to senior leadership/board-level stakeholders
  • Bachelor's or Master's degree in Computer Science, Engineering, or related discipline (STEM preferred)
  • 5+ years of people leadership, including hiring, performance management, coaching, and org design
  • Preferred: Experience in payments, fintech, or financial services (merchant onboarding, transaction processing, settlement, chargebacks, fraud/risk, regulatory reporting)
  • Preferred: Familiarity with data monetization, secure data sharing, and embedded analytics patterns for partners/merchants