Head of Enterprise Data Engineering

Global Payments is seeking a Head of Enterprise Data Engineering to define and execute theenterprise data engineering strategy, platforms, and operating model for AI-ready data at globalscale. This executive will lead the architecture, delivery, and operations of modern cloud dataplatforms and products, establish trusted data domains, and embed data governance and dataquality into automated pipelines. The role partners closely with engineering, product, risk andline-of-business leaders to accelerate data-driven outcomes, reduce time-to-insight, and enableAI/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 whiteboarddesigns, optimize pipelines, and resolve complex data challenges. Success looks like ameasurable 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 forAI-ready data, including cloud platform, data products, and automation-first deliverymodel. Develop and communicate the enterprise data strategy and roadmap, ensuringalignment with business transformation, regulatory needs, and future-proofing.● Lead architectural decisions for lakehouse patterns, streaming, CDC, and event-drivenintegration; balance reuse, performance, cost efficiency, and time-to-market.● Architect, implement, and operate hybrid and cloud-native data platforms with heavyautomation.● Establish trusted domains focusing on security, governance, and reuse across businesslines.Lead the design and delivery of reusable, trusted data products with clear SLAs,documentation, versioning, and APIs; enforce data contracts between producers andconsumers.● Enable secure, governed data sharing and monetization where appropriate.● Provide platform services and reusable capabilities for data science and AI: featurestore, model-ready curated layers, governed sandboxes, MLOps integration, andmodel/data lineage.● Embed data governance within pipelines: lineage capture, data classification, role-basedand attribute-based access, fine-grained controls, and consent management. ImplementDQ-by-design: thresholding, anomaly detection, reconciliation, and data SLAs enforcedin 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 buildingstrong relationships with executives, technical teams, vendors, and business partners tounderstand needs, influence strategy, and promote best practices.● Oversee large-scale data migration, modernization, and platform implementationprojects, balancing innovation, cost-effectiveness, and risk management.● Scale, mentor, and inspire a diverse, high-performing data engineering and architectureteam; develop adaptive hiring and resourcing strategies reflecting organizational growthand transformation.● Ensure compliance with all risk, regulatory, and audit standards, and maintain rigorousinternal controls.Required● 15+ years in engineering and/or data and analytics, including 8+ years leadinglarge-scale data engineering and platform teams in complex, regulated environments.● Deep expertise in data architecture and engineering: data modeling (OLTP/OLAP), bigdata 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 dataobservability.● Real-time data streaming expertise (e.g., Kafka, Pub/Sub, Kinesis), event-drivenarchitectures, and change data capture patterns.Proven success designing andoperating enterprise cloud-native data platforms on at least one hyperscaler● Practical experience enabling AI/ML: feature stores, model-ready datasets, MLOpsintegration, and privacy-preserving patterns; comfortable partnering with datascientists and ML engineers.● Executive presence with the ability to translate complex architectures into businessvalue, present to senior leadership/board-level stakeholders, and lead throughinfluence.● 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 with knowledge of domains suchas merchant onboarding, transaction processing, settlement, chargebacks, fraud/risk,and regulatory reporting.● Familiarity with data monetization, secure data sharing, and embedded analyticspatterns for partners/merchants.Core Competencies● Ability to define an enterprise-wide, AI-first data vision and convert it into anexecutable, value-centric roadmap.● Comfort whiteboarding and debating designs with senior engineers; fluency acrossstorage, compute, networking, security, and cost optimization.● Treats data as a product with clear consumers, SLAs, quality metrics, and lifecyclemanagement.● Drives measurable outcomes with clear OKRs; reduces time-to-insight; improvesreliability and lowers unit costs.● Attracts and develops top talent; creates a culture of craftsmanship, accountability,and continuous learning.Org Chart - Head of Enterprise Data Engineering1. Data Platform & Infrastructure Leada. Cloud Platform Engineeringb. Data Compute & Storagec. FinOps & Capacity Management2. Data Domain Engineering Leada. Data Domain Teamsb. Semantic Layer & APIs3. Data Integration & Streaming Leada. Ingestion & CDCb. Real-time/Streaming4. Data Quality Engineering Leada. DQ in CI/CD and Runtime (rules, anomaly detection, auto-remediation)b. Lineage/Metadata/Contracts5. Data Governance Engineering Leada. Governance-as-Code (ABAC/RBAC, PII classification, policy enforcement)b. Stewardship Tooling & Workflows; Data domains’ controls6. MLOps & AI Data Services Leada. Feature Store & Model-Ready Datasets; Offline/Online storesb. Model Ops Integration (with DS/AI and AI Governance)c. Reusable data capabilities and frameworks