Head of Enterprise Data Engineering at Global Payments

Alpharetta, Georgia, United States

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

FinTech, Payments, Financial ServicesIndustries

Requirements

15+ years in engineering and/or data engineering leadership

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