Head of Enterprise Data Engineering

Every day, Global Payments makes it possible for millions of people to move money between buyers and sellers using our payments solutions for credit, debit, prepaid and merchant services.  Our worldwide team helps over 3 million companies, more than 1,300 financial institutions and over 600 million cardholders grow with confidence and achieve amazing results.  We are driven by our passion for success and we are proud to deliver best-in-class payment technology and software solutions.  Join our dynamic team and make your mark on the payments technology landscape of tomorrow. 

Role Overview 

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. This executive will lead the architecture, delivery, and operations of modern cloud data platforms and products, establish trusted data domains, and embed data governance and data quality into automated pipelines. The role partners closely with engineering, product, risk and line-of-business leaders to accelerate data-driven outcomes, reduce time-to-insight, and enable AI/ML across the enterprise. 

The ideal leader is equal parts strategist and hands-on technologist capable of setting vision, shaping enterprise data architecture, and diving deep with senior engineers to whiteboard designs, optimize pipelines, and resolve complex data challenges. Success looks like a measurable increase in trusted data product adoption, accelerated delivery through automation, reduced total cost of ownership, and demonstrable business value from AI-ready data. 

Key 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. Develop and communicate the enterprise data strategy and roadmap, ensuring alignment with business transformation, regulatory needs, and future-proofing. 

● 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. 

● 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 by optimizing build-vs-buy decisions, licensing, cloud spend, and vendor relationships. Scale teams and partners globally while building strong relationships with executives, technical teams, vendors, and business partners to understand needs, influence strategy, and promote best practices. 

● Oversee large-scale data migration, modernization, and platform implementation projects, balancing innovation, cost-effectiveness, and risk management.

● Scale, mentor, and inspire a diverse, high-performing data engineering and architecture team; develop adaptive hiring and resourcing strategies reflecting organizational growth and transformation. 

● Ensure compliance with all risk, regulatory, and audit standards, and maintain rigorous internal controls. 

Required Experience

● 15+ years in engineering and/or data and analytics, including 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