AVP, Data Platform & Engineering

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:  is not available for this opportunity.

Requisition #: 75968

The Role at a Glance

Lincoln Financial is investing heavily in modern data and AI capabilities to drive business transformation at enterprise scale. As AVP, Data Platform & Engineering, you will serve as the senior technical leader responsible for architecting, building, and operating Lincoln’s modern data infrastructure—including the cloud data lakehouse, batch and streaming pipelines, feature store, and data platform services that power every AI and analytics use case across the firm.

This role sits within the AI, Data & Analytics organization and reports to the Chief Data & AI Engineering Officer. You will work in close daily partnership with Data Strategy & Governance (which defines logical models, standards, and requirements), AI/ML & Agentic Engineering (which consumes data for model training and inference), and the CIO organization (to ensure enterprise integration, security, and compliance).

This is a senior, hands on leadership role combining deep technical ownership with organizational scale, executive partnership, and measurable business impact.

What you'll be doing

Data Platform Architecture & Strategy 
•    Own end to end architecture for the data platform, including cloud lakehouse, pipelines, streaming, and feature store infrastructure optimized for AI
•    Define multi year platform roadmaps and lead strategic build vs buy decisions
•    Manage senior vendor relationships across Databricks, Snowflake, cloud providers, and orchestration platforms
•    Establish engineering standards (CI/CD, data contracts, observability, data mesh patterns)
•    Partner with the CIO organization to ensure alignment with enterprise systems, security, and IT governance
•    Collaborate with Data Strategy & Governance leaders to implement logical architectures and canonical standards
Data Engineering & Pipeline Execution 
•    Build and operate scalable ELT/ETL pipelines integrating data from 50+ legacy and modern systems
•    Implement data contracts, validation, monitoring, and lineage for AI critical data assets
•    Deploy and scale real time streaming architectures (Kafka/Kinesis) for low latency AI inference
•    Modernize legacy ETL tooling and migrate to cloud native platforms
•    Optimize performance, reliability, and cost across data storage and compute environments
Feature Store & AI Data Enablement 
•    Implement and run the enterprise feature store platform, translating logical requirements into production infrastructure
•    Deliver standardized data products and semantic layers enabling self service for AI teams and analytics users
•    Build pipelines supporting AI training data, testing datasets, and production inference
•    Implement AI specific data quality monitoring aligned to governance standards
•    Enable AI teams to develop, test, and deploy models independently and at scale
Platform Operations & Engineering Excellence 
•    Own platform reliability, deployment, monitoring, and operational excellence for all AI supporting data infrastructure
•    Build and lead a high performing team of data engineers and platform engineers
•    Implement comprehensive observability (pipeline health, freshness, quality, cost, uptime)
•    Ensure compliance with financial services regulatory requirements in partnership with InfoSec, Legal, and Compliance
•    Establish reusable frameworks, tooling, and automation to maximize engineering velocity and developer productivity

What Success Looks Like 

• Modern platform foundation established: Cloud lakehouse, pipelines, streaming, and feature store operational, with critical data migrated from legacy systems and AI ready data products delivered.
• Scalable, reliable execution model: Dozens of production pipelines, high platform uptime, and self service data access enabling AI and analytics teams to move independently and quickly.
• Enterprise grade maturity: Complete migration to a modern, federated data platform supporting multiple business domains, real time AI use cases, and production ML at scale.
• Demonstrable business value: A best in class data platform enabling 50+ AI models in production and delive