Global Payments is seeking a Head of Enterprise Data Engineering to define and execute the enterprise data engineering strategy, platforms, and operating model for AI-ready data at global scale.
Requirements
- 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.
- Experience in payments, fintech, or financial services with knowledge of domains such as merchant onboarding, transaction processing, settlement, chargebacks, fraud/risk, and regulatory reporting.
- Familiarity with data monetization, secure data sharing, and embedded analytics patterns for partners/merchants.
Responsibilities
- 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; balance reuse, performance, cost efficiency, and time-to-market.
- 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 the 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.
Other
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related discipline (STEM preferred).
- 15+ years in engineering and/or data and analytics, including 8+ years leading large-scale data engineering and platform teams in complex, regulated environments.
- 5+ years of people leadership, including hiring, performance management, coaching, and org design.
- Executive presence with the ability to translate complex architectures into business value, present to senior leadership/board-level stakeholders, and lead through influence.
- Ability to define an enterprise-wide, AI-first data vision and convert it into an executable, value-centric roadmap.
