Vice President / Head of Enterprise Technology Architecture

Trilliad

6h•$215,000 - $245,000

About The Position

The Vice President/Head of Enterprise Technology Architecture is accountable for Trilliad's enterprise technology architecture — the structure that determines how our core platforms fit together, the master data standards underneath them, the AI and automation architecture that governs how intelligence is embedded across systems, and the roadmap that sequences what we build next. This is a builder's role. You partner with leadership to define the target-state architecture across finance, talent, CRM, marketing, and operational systems; establish master data definitions and ownership where none existed; bring order to an integration landscape that has grown organically; and set the technical foundation that makes responsible AI use enforceable rather than aspirational. You lead a small internal IT team and manage our MSP relationship, ensuring end-user support and platform administration run reliably without consuming the strategic capacity of the function. You serve as the primary technology partner to Finance, Talent, Growth, and Marketing operations — translating business objectives into prioritized technology work, making trade-offs transparent to executive stakeholders, and building credibility as a trusted advisor who pushes back constructively when a request conflicts with the target-state architecture. You operate at the intersection of strategy and execution. You move between a data model diagram and a vendor escalation in the same afternoon. You explain to a non-technical executive why a given sequencing decision matters, and you apply the same architectural discipline to AI proposals as to any other investment. You own the business case for major technology investments, support vendor selection and contract negotiation, and ensure that as Trilliad integrates across service lines, the technology footprint scales with coherence rather than complexity.

Requirements

8+ years in enterprise systems, business systems, IT leadership, or solutions architecture, including direct ownership of a multi-platform environment where you were accountable for how systems integrated and evolved
Demonstrated experience designing and governing integrations across ERP, CRM, and HCM systems, with hands-on familiarity with at least two of Salesforce, NetSuite, Dayforce, Darwinbox, or HubSpot, in an environment where you defined integration patterns and system-of-record designations
Practical master data management experience in an organization that did not previously have formal data governance, where you defined entities, ownership, and stewardship and reconciled conflicting structures into a coherent enterprise model
Experience managing outsourced IT service providers, including SLA definition, vendor performance management, and cost control, in a role where you were accountable for the quality and reliability of the partnership
People management experience leading a small technical team spanning support and administration, with accountability for their development and performance
Track record of building and defending a technology roadmap with executive stakeholders, including translating business objectives into prioritized technology work and making trade-offs transparent when demand exceeded capacity
Contextual Awareness
Critical Reasoning and Thinking
Data/Analytic Acumen
Empathy
Judgment and Discernment
Moral Courage
Pattern Recognition and Systems Thinking
AI Literacy
Responsible and Effective AI Use
Trilliad’s Business and Commercial Context

Nice To Haves

Enterprise systems architecture and integration design — You design how multi-platform environments fit together, define system-of-record designations, and establish integration patterns that balance flexibility with architectural coherence, ensuring platforms connect reliably as the business scales
Master data modeling and governance — You define core entities, ownership, and stewardship in environments that did not previously have them, reconciling conflicting structures into a coherent enterprise model that supports reporting, automation, and AI
AI and automation architecture — You define the technical foundation for how AI and automation are deployed across the enterprise, including data access patterns, identity models, governance controls, and the boundary between vendor-embedded and Trilliad-built capability
Technology roadmap development and prioritization — You translate business objectives into sequenced technology work, build business cases for major investments, and make trade-offs transparent to executive stakeholders when demand exceeds capacity
Vendor and MSP management — You manage outsourced IT service providers end to end, including SLA definition, performance management, escalations, and cost control, ensuring the partnership delivers value without creating dependency
Technical communication to non-technical audiences — You move fluidly between technical detail and executive summary, explaining architectural decisions and constraints in business language that builds credibility and trust with service line and enablement leadership
Multi-platform enterprise system ecosystems — You understand how ERP, CRM, and HCM systems integrate in practice, including the data models, API capabilities, and integration patterns of platforms such as Salesforce, NetSuite, Dayforce, Darwinbox, and HubSpot, because this role requires you to define how these platforms fit together and where system-of-record accountability sits
Master data management frameworks and governance models — You know how to define entities, ownership, and stewardship in organizations that did not previously have formal data governance, because this role requires you to reconcile conflicting data structures and establish the data foundation that makes automation and AI reliable
AI platform capabilities, access models, and governance requirements — You understand how AI is embedded in enterprise platforms, how data access and permissioning work for AI and agents, and what governance controls are required to make responsible use enforceable, because this role owns the enterprise AI architecture and rollout
IT service management and support models — You know how to structure support between internal teams and outsourced providers, define SLAs that matter, and use service metrics to drive root-cause fixes, because this role manages both a small internal IT team and the MSP relationship
Technology investment business case development — You understand how to quantify cost, risk, and expected outcome for major technology investments, and how to evaluate vendor proposals with scrutiny of AI functionality and pricing, because this role owns the systems roadmap and supports executive decision-making

Responsibilities

Define and maintain the target-state technology architecture across finance, talent, CRM, marketing, and operational systems, including system-of-record designations and integration patterns.
Map current-state data flows and dependencies between platforms; identify redundancy, fragility, and manual workarounds, and build a plan to retire them.
Set standards for how integrations are built, documented, monitored, and supported — including API, event, and agent access patterns — whether delivered by internal staff, the MSP, or an implementation partner.
Review and approve significant system, integration, and configuration changes before they reach production, , including the deployment of AI features, agents, and automations that read from or write to systems of record.
Establish master data definitions, ownership, and stewardship for core entities — client, employee, vendor, project, and financial hierarchies — across service lines, , recognizing that these definitions are the precondition for reliable reporting, automation, and AI-generated output alike.
Reconcile conflicting data structures inherited from acquisitions and legacy implementations into a coherent enterprise model, , sequencing the work by where fragmented data most constrains decision-making and automation.
Stand up practical data quality monitoring and a governance forum with clear decision rights, sized to the organization rather than borrowed from an enterprise playbook, and extend its remit to cover decisions about AI and automation that touch core data.
Define the enterprise AI and automation architecture — approved platforms, data access patterns, identity and permissioning models, and the boundary between vendor-embedded AI capability and Trilliad-built capability.
Own the internal AI rollout end-to-end: tooling selection, provisioning, integration with systems of record, and the technical controls that make responsible use enforceable rather than aspirational.
Establish AI governance covering acceptable use, data classification and residency, vendor and model review, an inventory of deployed AI and agents, and audit traceability — embedded in architecture rather than resting on policy alone.
Identify and sequence automation opportunities across finance, talent, and operational processes, distinguishing work that should be automated from work that should first be simplified or retired.
Partner with service line and enablement leadership to make enterprise data and systems accessible to AI-enabled workflows, ensuring client and employee data protections hold as that access broadens.
Build internal capability to design, deploy, and support AI and automation, and define what production-ready means for AI-assisted and agentic systems.
Own the systems roadmap: intake, evaluation, sequencing, and communication of technology initiatives across the business.
Translate business objectives into prioritized technology work, and make trade-offs transparent to executive stakeholders.
Build the business case for major investments, including cost, risk, and expected outcome; support vendor selection and contract negotiation, , with particular scrutiny of AI functionality and pricing in platform renewals.
Manage the MSP relationship end to end — scope, SLAs, performance reviews, escalations, and cost.
Lead and develop a small internal IT team responsible for ticket triage, end-user support, and day-to-day platform administration.
Define the support model: what the MSP handles, what stays internal, and how work escalates between them, how work escalates between them, and where accountability sits for supporting AI and automation tooling.
Track service metrics that matter (resolution time, recurring issue themes, user satisfaction) and use them to drive root-cause fixes rather than faster patching.
Maintain appropriate security, access, and compliance controls across systems — including data loss prevention and access review for AI tooling — in partnership with internal stakeholders and the MSP.
Serve as the primary technology partner to service line leadership, finance, HR, and marketing operations.
Communicate architectural decisions and constraints in business language to non-technical audiences.
Build credibility as a trusted advisor rather than an order-taker — pushing back constructively when a request conflicts with the target-state architecture, and applying the same discipline to AI proposals as to any other investment.
Lead and line-manage the internal IT team