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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Enterprise Architect - **Company:** KAPITUS LLC - **Location:** Washington, DC, United States (Remote available) - **Experience:** Expert - **Salary:** $117,800 - $189,000 - **Contract:** Permanent contract - **Skills:** OpenTofu, PHP (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Data Infrastructure, Data Stores, Disaster Recovery, Middleware, Machine Learning, NoSQL, Salesforce.Com, Software Engineering, Lexis, System Availability, Snowflake, Togaf, Event Driven Architecture, Terraform, Webhooks, Data Pipelines, Api Management, Serverless Computing, Legacy Systems - **Published:** October 26, 2026 - **Apply:** https://startup.jobs/enterprise-architect-kapitus-10525138 ## About the Role * 10+ years experience in software engineering and architecture, several of them as an enterprise or solution architect owning complex, high-volume transactional systems end to end. * Deep cloud-native architecture on AWS: containers and serverless, event-driven patterns, API management, relational and NoSQL data stores, and the security and cost controls around them. * Strong data and analytics architecture: canonical and semantic models, warehouse and lakehouse patterns (for example Snowflake), the operational-versus-analytical data split, integration and adapter patterns, and comfort operating across the seam where a data organization and engineering meet. * A track record modernizing legacy systems in production, including strangler and incremental-migration approaches that avoid big-bang risk. * Security-and-compliance-by-design instincts, ideally in a regulated industry such as lending, fintech, banking, or insurance. * Exceptional cross-functional influence: you build architecture with people, earn trust across product and engineering, and communicate clearly up to the board and down to the keyboard. * A pragmatic, outcome-first mindset: you sequence to business value, make the call when evidence is sufficient, and document the why. Nice to Haves: * Salesforce platform architecture at scale, and experience with a modern lending or core-banking platform (for example Mambu or similar). * Runtime and framework modernization (for example PHP major-version uplift), and microservice decomposition of a monolith. * Credit-decisioning ecosystems: bureau and data vendors (Experian, LexisNexis, Plaid, and cash-flow providers), and model deployment and model-risk governance. * Familiarity with AI-assisted development tooling and guardrails, and with infrastructure-as-code (Terraform or OpenTofu). * A recognized architecture framework (for example TOGAF) used as a tool. ## Description This is a build-and-shape role, not a maintain role. You will define the target-state architecture for the entire lending lifecycle, stand up the architecture practice that keeps it coherent, and sequence the modernization that unlocks the next phase of growth. You will work shoulder to shoulder with engineering, product, data, and security leaders, and your decisions will reach the platform that moves real money for real businesses. What You Will Do: * Own the end-to-end architecture. Produce and maintain a living, current, AI-crawlable map of how a deal flows from lead through decisioning, funding, servicing, and collections, and the systems, data stores, integrations, and owners behind it. Make it the single reference the whole organization trusts. * Set the target state and sequence the path to it. Define the modernization roadmap, including the core-platform runtime uplift, decoupling decisioning from the core, the move toward a modern lending core, and the environments and infrastructure-as-code that let change ship safely. Sequence the work against the product roadmap so each initiative unblocks a business outcome. * Establish the canonical data model. Define Kapitus-owned data models for the core decisioning inputs (credit bureau, cash flow, KYB), and a vendor-adapter layer so data providers plug in behind a stable internal contract. This removes single-vendor lock-in and enables failover and champion-challenger testing. * Integrate the data and AI architecture, and keep it coherent. Partner closely with the Chief Data and AI Officer's organization so the data platform, the analytics and reporting products, the cross-team decisioning-data pipeline, and the machine-learning and model-deployment lifecycle fit cleanly into the enterprise architecture. Hold the operational-versus-analytical data split (real-time to the transactional store, analytical to the warehouse), define the ownership boundaries and contracts where data, engineering, and product meet so nothing sits orphaned between organizations, and maintain how that domain fits the overall picture as it evolves. * Lead integration and event-driven architecture. Shape the API and eventing standards (REST, webhooks, event buses), the partner-API contract, and the middleware that connects Salesforce, the core platform, AWS services, and third parties. * Design for security and resilience from the start. Make authentication, secrets management, least-privilege, audit logging, disaster recovery, high availability, and observability first-class attributes of every design, in a regulated-lending context. * Stand up architecture governance. Run a lightweight, respected practice: architecture principles, decision records (ADRs) with real trade-off analysis, a design-review gate, and build-versus-buy and total-cost-of-ownership discipline. Keep it enabling, not bureaucratic. * Partner, do not dictate. Bring product, engineering, the data and AI organization, and security into architecture decisions early through joint, rolling planning, and translate technical choices into business terms for executives and into clear guidance for engineers. * Govern responsible AI enablement. Shape the guardrails and standards for AI-assisted development and for model deployment and governance, in partnership with the data and security organizations. * Raise the bar across teams. Mentor engineering managers and technical leads, grow architectural thinking across the group, and leave the organization stronger than you found it., * A socialized, trusted end-to-end architecture map and a system-and-ownership inventory the whole organization uses, including during incidents. * An approved target-state architecture and a sequenced modernization roadmap tied to the product plan and the funding case. * A canonical data model and vendor-adapter standard adopted on the decisioning path, with a clear route to vendor failover. * A running architecture practice: principles, decision records, and a design-review gate that engineers value. * The data and AI domain integrated into the enterprise architecture, with the data-to-engineering seams, ownership, and contracts defined and agreed with the Chief Data and AI Officer's team. * A measurable drop in architecture-driven incidents and rework, and faster, safer delivery of committed initiatives.