Head, Data & AI Platforms and Architecture

Job Posting End Date: October 23
Our Fortune 500 company is driving a digital transformation and looking for forward-thinking innovators to disrupt how our industry thinks about and uses technology. As one of the world's leading employee benefits providers, we help millions of people gain affordable access to benefits that help them protect their families, their finances and their futures.
Are you an asker of questions, a solver of problems, and a challenger of the status quo? Our mission is to provide a differentiated customer experience and exceed the expectations people have of technology at any company — not just insurers.
We are seeking individuals to join our team of talented IT professionals who share never-ending passion and an unwavering focus on our customer experience. Team members comfortable working in an agile, fast-paced, and delivery-focused environment thrive in our environment where we value an entrepreneurial spirit and those who challenge the status-quo.
Unum is changing, and we’re excited about what’s next. Join us.
General Summary:Head of Platforms & Architecture is the single accountable executive for the technology foundation on which Unum's data, analytics and AI ambitions are built. This leader owns the strategy, architecture, and governance of Unum's enterprise data platforms, agentic AI and AI governance platforms, and business intelligence and data marketplace platforms.
This leader must be equally credible in a deep technical design review, in a vendor negotiation with a hyperscaler or platform provider, and in front of the Executive Committee or Board describing the business value of the platform investment.
The incumbent leads a multi-disciplinary organization of data architects, AI architects, data modelers, and sets the technical standards, reference architectures and guardrails followed across all of Unum's data and AI delivery teams.
Principal Duties and Responsibilities
Define and own the multi-year enterprise data platform strategy, including the target-state lakehouse/warehouse architecture, platform-of-record decisions, coexistence and consolidation strategy, and the migration roadmap away from legacy on-premises data warehouses, mainframe extracts, and point-to-point integrations.
Establish and enforce reference architectures for ingestion, storage, transformation, orchestration, and serving. Set standards for data contracts, schema evolution, data product interfaces, and interoperability.
Provide the shared frameworks, templates, CI/CD pipelines, testing harnesses and reusable components that allow business-aligned data engineering teams to deliver quickly and consistently on the platform, rather than reinventing plumbing.
Own the platforms for master and reference data management
Ensure the data platform meets enterprise SLAs for availability, recovery, performance, and scalability; embed security-by-design in partnership with Information Security and Enterprise Risk.
Own the cloud and platform consumption model; implement FinOps practices and be accountable for unit-cost trends and total cost of ownership of the data estate.
Lead the decommissioning of redundant data stores, ETL tools and reporting marts, quantifying and delivering run-cost savings and risk reduction.
Ensure platform controls, evidence and documentation satisfy internal audit, model risk management, SOX ITGCs, state insurance regulators, and requirements.
Define and deliver Unum's enterprise platform for building, deploying, orchestrating, and governing AI agents and GenAI applications — including foundation-model access, model gateways, retrieval, agent orchestration and tool-calling frameworks, memory, and human-in-the-loop controls.
Evaluate and select agentic and AI platform capabilities across Databricks, Snowflake, Microsoft, and specialized vendors; define where each fit, how they interoperate and how the enterprise avoids fragmentation and lock-in.
Own the end-to-end lifecycle tooling for classical ML, GenAI and agentic workloads: feature stores, experiment tracking, model registry, prompt and agent versioning, evaluation harnesses, red-teaming, deployment, monitoring for drift, hallucination, toxicity, cost, and latency.
Build and operate the technical control plane for Responsible AI — model and agent inventory, risk tiering, approval workflows, policy enforcement, guardrails, audit logging, explainability and bias testing — aligned to Unum's AI governance framework, Model Risk Management, NAIC AI Model Bulletin expectations, Colorado SB 21-169 and similar state rules, the EU AI Act (for UK/Poland operations) and NIST AI RMF. Partner with Legal, Compliance, Privacy, Enterprise Risk, and the AI Governance Committee.
Own the enterprise data catalog, lineage, policy, and access-governance tooling that underpins both data and AI governance, so that data owners and stewards across the business can operate governance at scale.
Partner with business and product leaders to move agentic AI from pilot to production in priority domains ensuring each is built on the platform, is observable and is governed.
Set standards for prompt-injection defense, data-leakage prevention, secure tool access, agent permissions and least privilege, secrets management and incident response for AI systems, in partnership with Information Security.
Maintain executive relationships with strategic partners; stay ahead of the rapidly evolving agentic landscape and translate it into pragmatic roadmap decisions for Unum.
Own the enterprise BI and semantic-layer platform (, including standards for certified datasets, metric definitions, performance, capacity management, embedded analytics, and self-service enablement across finance, actuarial, claims, sales, and operations.
Stand up and scale an internal data marketplace where certified data products, semantic models, ML features and AI assets are discoverable, requestable, contract-backed and consumable with governed access — shifting the enterprise from ad hoc extracts to reusable, owned data products.
Provide secure, governed capabilities for data sharing with brokers, employers, TPAs, reinsurers, regulators, and partners
Lead the migration of legacy reporting onto the modern platform, retiring duplicative reports and marts.
Measure and drive adoption of self-service BI and marketplace assets; partner with the Data & Analytics community of practice on training and enablement.
Serve as the enterprise's chief architect for data and AI. Own the target-state architecture spanning source systems, integration, data platform, AI platform, BI/marketplace and consuming applications, and the transition architecture to get there.
Chair the Data & AI Architecture Review Board; define principles, patterns, standards, and exceptions processes; ensure every material data or AI initiative across Unum US, Colonial Life, Unum International and Corporate functions aligns to the target architecture or has an approved, time-boxed exception.
Own the enterprise conceptual and logical data models for insurance domains (party, group/employer, product, policy/certificate, coverage, enrollment, billing, claim, absence/leave, provider, agent/broker, financial) and the modeling standards used across teams.
Partner with the Enterprise Architecture, Application, and Integration teams so that core administration modernization (policy admin, claims, billing), digital and API programs and the data/AI platforms evolve coherently.
Continuously evaluate emerging technologies and vendors, run structured proofs-of-concept, and make disciplined build/buy/partner recommendations with clear business cases.
Lead, inspire and develop a multi-disciplinary organization of enterprise and data architects, AI architects, data modelers through a team of AVP/Director-level leaders.
Design and evolve the operating model for platforms and architecture — including the balance between central platform teams and federated, business-aligned delivery teams; the role of architects embedded in domains; and the interfaces with Data Governance, Data Science, Information Security and Infrastructure.
Own the talent strategy for scarce, high-demand skills. Build career paths and skills frameworks, upskill the existing workforce from legacy ETL/warehouse skills to modern platform and AI skills, and attract external talent in a competitive market.
Instill practices of platform-as-a-product thinking, DevOps/DataOps/MLOps automation, documentation, blameless post-mortems, inner-sourcing and measurable reliability; hold leaders accountable for engineering quality and delivery predictability.
Set clear goals and accountabilities, conduct rigorous talent reviews, coach leaders, and build succession depth for all critical roles including the incumbent's own.