Senior Machine Learning Scientist, AI
Elevance Health
CloudKafkaRaySparkSQL
Head of Enterprise Data Engineering
Global Payments Inc.
full-time
Posted on:
Origin: • 🇺🇸 United States
Visit company websiteJob Level
Lead
Tech Stack
CloudKafka
About the role
- 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
- 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 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 and optimize build-vs-buy decisions, licensing, cloud spend, and vendor relationships
- Oversee large-scale data migration, modernization, and platform implementation projects
- Scale, mentor, and inspire a diverse, high-performing data engineering and architecture team; develop adaptive hiring and resourcing strategies
- Ensure compliance with all risk, regulatory, and audit standards and maintain rigorous internal controls
Requirements
- 15+ years in engineering and/or data and analytics
- 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 ability to translate complex architectures into business value and present to senior leadership/board-level stakeholders
- 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 (merchant onboarding, transaction processing, settlement, chargebacks, fraud/risk, regulatory reporting)
- Preferred: Familiarity with data monetization, secure data sharing, and embedded analytics patterns for partners/merchants
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