Developer/Architect

Our client is evaluating upgrades to Financial Design GUI, report generation capabilities, and the calculation engine currently powered by DXC VP/MS with Financial customizations. The target direction is to support a more modular, API-driven product calculation and rules capability that can be wrapped, abstracted, and incrementally modernized.

AI Expectation

Use AI-assisted analysis and development tools such as Claude AI and GitHub Copilot, where appropriate, to accelerate discovery, documentation, code analysis, and prototyping.

Primary Outcome

Produce actionable discovery findings, options, risks, dependencies, and roadmap recommendations for Financial planning and vendor alignment.

Financial SME Partnership Model

Financial will provide ongoing subject matter expertise and guidance throughout the engagement. Vendor resources are expected to operate in a collaborative discovery model, partnering closely with internal Financial Product Owners, Calculation SMEs and Leads, Enterprise Architects, Solution Architects, BIRT SMEs, Engineering Leads, and QA partners.

• Use Financial SME input to accelerate current-state discovery and validate understanding of business rules, calculation flows, reporting dependencies, and platform constraints.
• Partner with Product Owners and Calculation SMEs/Leads to clarify product behavior, actuarial logic, customizations, validation expectations, and business priorities.
• Partner with Enterprise Architects and Solution Architects to align findings with target-state architecture, modernization patterns, API/service direction, and sequencing options.
• Document questions, assumptions, decisions, risks, dependencies, and open items in a format that Financial SMEs can review and confirm.
• Do not independently define future-state recommendations without Financial SME review; recommendations should be co-developed and grounded in Financial architecture, product, and operational context.

Resources should be comfortable working where deep Financial domain knowledge is supplied by internal SMEs and the vendor contribution is structured discovery, analysis, technical assessment, documentation, and modernization planning.

Responsibilities

• Analyze the technical architecture of the existing calculation platform, including VP/MS model usage, integration points, data inputs, and calculation outputs.
• Reverse engineer Financial-specific customizations, interfaces, and dependencies around the DXC VP/MS-powered calculation engine.
• Perform BA-style discovery with business and technical stakeholders to capture requirements, pain points, current-state flows, and decision points.
• Evaluate technical upgrade paths for Financial Design GUI and related calculation authoring or modeling capabilities.
• Assess API, service, batch, reporting, and downstream integration touchpoints that depend on calculation outputs.
• Partner with BIRT reporting SMEs to understand report engine integration points, data dependencies, and upgrade considerations.
• Identify candidate areas for wrapping, abstraction, modularization, or replacement using product rules, pricing, projection, and scenario services.
• Use Claude AI, GitHub Copilot, and approved tools to accelerate code analysis, documentation, and proof-of-concept work.

Requirements

Required Skills / Experience

• Engineering depth: 10 years of enterprise software engineering, architecture, or legacy-system modernization experience.
• Calculation systems: Experience with product calculation engines, rules engines, pricing logic, projections, or highly configurable business rules platforms.
• BA/discovery skillset: Stakeholder interviewing, requirement elicitation, process and dependency mapping, current-state documentation, gap/risk identification, and business-facing recommendations.
• Integration architecture: Strong understanding of APIs, service orchestration, data contracts, dependency mapping, and enterprise integration patterns.
• Legacy assessment: Ability to reverse engineer older platforms, document technical debt, identify coupling, and recommend phased modernization options.
• Insurance technology: Experience in life insurance, annuities, illustrations, product definition, policy administration, or financial services technology.
• Communication: Ability to translate technical findings into executive-ready recommendations, risks, and roadmap options.

Preferred Skills / Nice-to-Have

• Experience with VP/MS or DXC platforms: Hands-on experience with DXC VP/MS, Vantage, Assure Product, or comparable product/modeling platforms.
• BIRT reporting & Office 365: Experience with BIRT report engine configuration, templates, data sources, rendering, or modernization, along with O365 expertise.
• AI-assisted engineering: Practical use of Claude AI, GitHub Copilot, or similar tools for code analysis, refactoring, documentation, and POC acceleration.
• Cloud/API modernization: Experience designing modular product services, calculation services, decision services, or strangler-pattern modernization approaches.

Expected Deliverables

• Technical architecture assessment for calculation platform, Design GUI, reporting, and integration dependencies.
• Inventory of critical VP/MS customizations, interfaces, calculation touchpoints, and modernization constraints.
• BA-quality discovery documentation including requirements, stakeholder inputs, current-state flows, gaps, and risks.
• Upgrade options analysis for Financial Design GUI and calculation engine components.
• Conceptual target-state architecture for modular product rules, pricing, scenario, and calculation services.
• Risk, dependency, validation, and sequencing recommendations for phased modernization.

Engagement Success Criteria

• Technical discovery produces enough detail to support vendor sizing, roadmap planning, and architecture decisions.
• Financial has clear recommendations on what to wrap, refactor, upgrade, prototype, or defer.
• Risk areas such as calculation/result parity, actuarial validation, reporting dependencies, and integration complexity are clearly identified.
• Developers can operate independently in discovery conversations, not just implementation tasks.