Head of Enterprise Data Engineering

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

● 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 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 the ability to translate complex architectures into business

value, present to senior leadership/board-level stakeholders, and lead through

influence.

● 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 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.

Core Competencies

● Ability to define an enterprise-wide, AI-first data vision and convert it into an

executable, value-centric roadmap.

● Comfort whiteboarding and debating designs with senior engineers; fluency across

storage, compute, networking, security, and cost optimization.

● Treats data as a product with clear consumers, SLAs, quality metrics, and lifecycle

management.

● Drives measurable outcomes with clear OKRs; reduces time-to-insight; improves

reliability and lowers unit costs.

● Attracts and develops top talent; creates a culture of craftsmanship, accountability,

and continuous learning.

Org Chart - Head of Enterprise Data Engineering

1. Data Platform & Infrastructure Lead

a. Cloud Platform Engineering

b. Data Compute & Storage

c. FinOps & Capacity Management

2. Data Domain Engineering Lead

a. Data Domain Teams

b. Semantic Layer & APIs

3. Data Integration & Streaming Lead

a. Ingestion & CDC

b. Real-time/Streaming

4. Data Quality Engineering Lead

a. DQ in CI/CD and Runtime (rules, anomaly detection, auto-remediation)

b. Lineage/Metadata/Contracts

5. Data Governance Engineering Lead

a. Governance-as-Code (ABAC/RBAC, PII classification, policy enforcement)

b. Stewardship Tooling & Workflows; Data domains’ controls

6. MLOps & AI Data Services Lead

a. Feature Store & Model-Ready Datasets; Offline/Online stores

b. Model Ops Integration (with DS/AI and AI Governance)

c. Reusable data capabilities and frameworks