Enterprise Technical Architect

Lorven Technologies Inc.; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Lorven Technologies Inc. seeks an FP&A Enterprise Technology Architect to lead the design and delivery of a modern FP&A, analytics, and AI platform using Oracle Fusion, FDI, AI Lakehouse, OAC and Power BI.

You will govern semantics, KPIs, and a single source of truth, while delivering scalable analytics and AI capabilities across Finance and Actuarial functions.

Qualifications

• Experience leading FP&A technology programs.
• Proficiency with Oracle Fusion and analytics stack.
• Ability to translate finance needs into scalable architecture.

Responsibilities

• Lead delivery across architecture, analytics, AI and reporting.
• Oversee design, build, test and deployment activities.
• Manage quality, risks and dependencies of solutions.
• Ensure alignment with EA standards and governance.
• Mentor architects, engineers, analysts and AI practitioners.
• Partner with Finance, FP&A, Actuarial and IT stakeholders.
• Develop roadmaps, business cases and transformation plans.
• Present recommendations to executive leadership.
• Drive alignment and change adoption.
• Design ledger‑aligned semantic layer using FDI.
• Define KPIs, hierarchies and governance rules.
• Establish single source of truth across domains.
• Create reporting with OAC and Power BI; dashboards.
• Enable governed self‑service analytics.
• Integrate datasets from finance, actuarial and enterprise.

Skills

FP&A Architecture

Solution Architecture

Stakeholder Management

Data Governance

AI & Analytics

Tools

Oracle Fusion

Oracle FDI

Oracle AI Lakehouse

Oracle Analytics Cloud

Power BI

EPBCS

Job description

Technologies: Oracle Fusion, Oracle FDI, Oracle AI Lakehouse, Power BI, Analytics Cloud

Job Description: FP&A Enterprise Technology Architect

Lead the design and delivery of a modern FP&A, Analytics, and AI platform leveraging Oracle Fusion, Fusion Data Intelligence (FDI), Oracle AI Lakehouse, EPBCS, Oracle Analytics Cloud (OAC), and Power BI. The role is responsible for establishing a governed semantic foundation, enabling advanced reporting and analytics, and delivering finance-focused AI and agent-based solutions.

Key Responsibilities

Delivery Leadership & Solution Execution

Provide end-to-end leadership for solution delivery, ensuring the successful implementation of the target-state architecture while balancing governance, scalability, business value, and delivery timelines.

• Lead phased delivery across architecture, analytics, AI, and reporting.
• Oversee design, build, testing, and deployment activities.
• Manage solution quality, risks, and dependencies.
• Ensure alignment with enterprise architecture and governance standards.
• Mentor architects, engineers, analysts, and AI practitioners.

Stakeholder Engagement & Business Transformation

Serve as the bridge between Finance, Actuarial, IT, and Executive Leadership by translating business objectives into a practical modernization roadmap and ensuring successful adoption of new capabilities.

• Partner with Finance, FP&A, Actuarial, and IT stakeholders.
• Facilitate architecture and strategy workshops.
• Develop roadmaps, business cases, and transformation plans.
• Present recommendations to executive leadership.
• Drive organizational alignment and change adoption.

FP&A Data & Semantic Architecture

Own the design of a governed finance data foundation by establishing ledger-aligned semantics, standardized KPIs, and reusable business definitions that enable consistent reporting, planning, analytics, and AI across Finance and Actuarial functions.

• Design and implement a ledger-aligned semantic layer using FDI.
• Define and govern KPIs, hierarchies, and business rules.
• Establish a single source of truth across Finance, FP&A, and Actuarial.
• Ensure end-to-end traceability from KPI to transaction-level detail

Lead the development of a modern analytics and reporting ecosystem using Oracle Analytics Cloud and Power BI, enabling business users to transition from static reporting to self-service, insight-driven decision-making.

• Design reporting solutions using OAC and Power BI.
• Build executive dashboards and management reporting.
• Define visualization standards and reporting frameworks.
• Enable governed self-service analytics.
• Integrate financial, actuarial, and enterprise datasets into reporting platforms.

AI Lakehouse & Advanced Analytics

Architect and deliver a scalable AI-ready data platform that unifies finance, actuarial, and enterprise data to support advanced analytics, predictive insights, forecasting, and emerging AI use cases.

• Design and implement Oracle AI Lakehouse.
• Enable advanced analytics and predictive capabilities.
• Support scenario modeling and forecasting.
• Develop enterprise data models supporting AI and machine learning.
• Integrate operational, financial, actuarial, and external data domains.

Fusion AI and Agent development

Drive the adoption of AI-enabled finance capabilities by building intelligent agents and copilots using Fusion Agent Studio that automate analysis, improve decision support, and enhance user productivity.

• Design and implement AI-enabled finance use cases.
• Build and configure AI agents using Fusion Agent Studio.
• Develop workflows for variance analysis, forecasting assistance, and financial insights.
• Establish AI governance and security controls.
• Support adoption of agent-based finance experiences.

Align planning, forecasting, and performance management processes with governed financial semantics to ensure consistency between actuals, plans, forecasts, and management reporting.

• Integrate EPBCS with FDI and AI Lakehouse.
• Align planning models with standardized KPI definitions.
• Support budgeting, forecasting, and variance analysis processes.
• Improve consistency across planning and actuals reporting.

Data Governance & Operating Model

Establish the policies, controls, and governance framework required to maintain trusted, compliant, and audit-ready financial data while supporting broad business consumption and AI enablement.

• Define governance processes for KPIs, hierarchies, and semantics.
• Establish standards for data quality and lineage.
• Implement metadata and auditability controls.
• Define ownership and stewardship models.
• Support regulatory and compliance requirements.