SVP, Chief Data and AI Engineering Officer

Alternate Locations: Radnor, PA (Pennsylvania); Charlotte, NC (North Carolina); Fort Wayne, IN (Indiana); Greensboro, NC (North Carolina)

 

 Work Arrangement:

Hybrid : Employee will work 3 days a week in a Lincoln office

 

Relocation assistance:  will be considered/provided for this opportunity within our company guidelines.

 

Requisition #: 75726

 

The Role at a Glance

Lincoln is seeking an SVP, Chief Data & AI Engineering Officer—a transformational leader who will architect the data and infrastructure foundation essential to powering the company’s AI driven future. This role requires someone highly entrepreneurial, with a track record of building and scaling high performing teams, while simultaneously delivering both foundational technical platforms and production ready AI products.

In this role, you will define and execute the enterprise data and AI engineering strategy, ensuring that Lincoln has the platforms, pipelines, governance, and operational excellence required to accelerate AI at scale. You’ll build the modern, governed, cloud native data and AI ecosystem that enables rapid experimentation, responsible AI, production deployment, and enterprise wide value realization. You’ll partner closely with AI Product & Delivery, Divisional CIOs, and senior business leaders to ensure data becomes a strategic asset that fuels innovation, improves operations, and drives competitive advantage.

What you'll be doing

Enterprise Data & AI Engineering Leadership

• Define & execute strategy: Lead the multi-year enterprise roadmap for data and AI engineering aligned with AI transformation and business priorities.
• Governance & stewardship: Establish and chair the enterprise data governance council; define data ownership, stewardship, and responsible AI governance frameworks.
• Architect for scale: Build modern, cloud-native data infrastructure—including storage, compute, streaming, and batch capabilities—to support high-throughput, low-latency AI workloads.
• Shape enterprise architecture: Develop logical architectures, semantic models, ontologies, and domain definitions tailored for AI consumption.

AI Solution Delivery & Product Partnership

• Lead rapid experimentation and quick proof of concepts for priority AI use cases.
• Guide MVPs through scalable, production ready pathways.
• Lead hands on technical design, including model approach, data needs, and solution architecture in partnership with AI product teams.

Data Platforms, Pipelines & MLOps

• Build robust data pipelines: Oversee ingestion, transformation, and orchestration pipelines engineered for reliability, scalability, and reusability.
• Enable end-to-end MLOps: Implement enterprise capabilities such as feature stores, model registries, CI/CD, automated testing, deployment, and rollback.
• Operationalize responsible AI: Embed model observability, drift detection, performance monitoring, lineage, and audit trails to meet regulatory and ethical standards.

Reliability, Security & Optimization

• Champion security & compliance: Ensure enterprise-level data security, privacy controls, access governance, and regulatory compliance across all platforms.
• Drive operational excellence: Lead Site Reliability Engineering (SRE), incident response, and capacity planning to meet availability and performance targets.
• Optimize cloud economics: Implement FinOps practices—right-sizing, autoscaling, workload optimization—to ensure cost-efficient platform operations.

Enablement, Partnerships & Culture

• Deliver trusted data products: Lead the design of data products, catalogs, and self-service tooling that empower AI, analytics, and business teams.
• Lead organizational change: Drive data literacy, enablement, and cultural transformation to accelerate enterprise-wide AI readiness.
• Manage strategic partnerships: Oversee vendor relationships and evaluate build-vs-buy decisions across platforms, tools, and data services.
• Influence at the executive level: Communicate strategy, risk posture, and engineering outcomes in clear business terms to senior leadership.

What we’re looking for

Must Haves:

• Bachelor’s degree plus 15+ years of progressive experience in large-scale data management, platform engineering, or software engineering supporting AI/ML.
• 7-10+ years in senior leadership with proven experience scaling teams from <10 to 30+ people, overseeing large engineering organizations with accounta