Senior Manager, AI and Data Engineering

Responsibilities

• Lead and develop a high-performing organization of data engineering, AI engineering, and data management professionals, providing coaching, performance management, career development, and succession planning.
• Define and execute the strategy, roadmap, and delivery model for enterprise data products, AI platforms, and knowledge assets aligned to Participant Financial Success business objectives.
• Build and scale reusable, governed data products that enable trusted analytics, reporting, personalization, marketing intelligence, sales enablement, and AI-driven decision-making.
• Establish AI-ready data foundations by advancing semantic intelligence, metadata management, data lineage, knowledge layers, discoverability, and business context across critical data assets.
• Oversee the design, implementation, and operation of scalable cloud-native data platforms, pipelines, services, and lakehouse architectures that support business, analytical, and AI workloads.
• Drive modernization initiatives through AWS-based platforms, automation, DataOps, MLOps, platform engineering, and emerging AI engineering capabilities.
• Partner with senior leaders across Participant Financial Success, Workplace Solutions, Technology, Product, and Analytics to prioritize investments, align delivery plans, and maximize business impact.
• Establish and enforce standards for data governance, quality, privacy, security, observability, compliance, responsible AI, and operational resilience.
• Lead initiatives that break down data silos and create connected data ecosystems, improving accessibility, interoperability, and enterprise-wide data reuse.
• Deliver AI-powered analytics and conversational intelligence capabilities that empower business users to generate trusted insights through natural language interactions.
• Manage platform adoption, reliability, service levels, operational performance, and value realization metrics to ensure solutions deliver measurable outcomes.
• Recruit and develop top talent while fostering a culture of innovation, continuous learning, engineering excellence, and accountability.
• Participate in special projects and perform other duties as assigned.

Qualifications

• Bachelor's degree or equivalent combination of training and experience required; advanced degree preferred.
• Minimum of ten years of progressive experience in data engineering, data management, analytics, AI/ML engineering, or related technology disciplines.
• Demonstrated experience leading managers and technical leaders within large-scale engineering organizations, including oversight of geographically distributed teams, contractors, and vendor partners.
• Proven success developing and executing enterprise data strategies, modern data platforms, and AI-enabled solutions that deliver measurable business outcomes.
• Strong understanding of data governance, metadata management, lineage, data quality, master data, privacy, security, and regulatory compliance practices.
• Experience implementing modern engineering disciplines including DataOps, MLOps, automation, platform engineering, and software delivery best practices.
• Knowledge of AI/ML platforms and technologies, including semantic layers, knowledge systems, conversational analytics, generative AI, and AI-ready data architectures.
• Preferred experience with Databricks, Apache Iceberg, Kafka/Confluent, SageMaker, and modern data observability or governance platforms.
• Strong program leadership, stakeholder management, and executive communication skills with the ability to influence across technical and business functions.
• Demonstrated ability to translate complex technical concepts into strategic business value and actionable outcomes.