**Job Description:** ### Required Technical Skills - ### **C# / .NET Core 8+** – deep understanding of the existing backend; required to assess, refactor, and migrate to modern architecture - **Python** – modern backend development, AI/ML integration, data pipelines, automation scripting, and rapid prototyping of replacement services; required across all three roles - **JavaScript / TypeScript** – full-stack capability for modern service development, API layers, and Node.js tooling - **JSON** – schema design, API contracts, configuration-as-code, LLM function calling specifications, structured data interchange - **Markdown** – documentation-as-code: ADRs, AI constitutions, specification documents, runbooks - **Docker / Kubernetes (EKS)** – containerised deployment and orchestration; Helm charts and CI/CD pipelines (Jenkins / GitLab) - **Database engineering** – SQL Server, Oracle RDS, and modern alternatives (PostgreSQL, columnar stores such as ClickHouse); stored procedures, query optimisation, and schema migration - **Data modelling & analysis** – designing data schemas for the replacement platform; understanding costing WBS trees, commodities, elements, and financial factors; data quality frameworks and analytical pipelines - **GitHub Copilot and Claude Code** – AI-first development as the default working mode, not an optional add-on - **LLM integration** – using AI models to replace rigid business logic with intelligent, adaptable solutions - **API-first design** – RESTful, GraphQL, and event-driven patterns for loosely-coupled architectures ### **Advantageous Skills** - ### **Platform migration / replatforming** – strangler fig pattern, parallel running; experience shipping large-scale migrations - **Event streaming** – Kafka, EventBridge as replacement strategies for complex Saga chains - **Redis, RabbitMQ / MassTransit** – understanding current patterns to inform migration strategy - **ClickHouse or modern analytics alternatives** – columnar analytics for costing and reporting data - **Frontend frameworks**: Angular 2+, React / Redux (awareness level is sufficient) - **Python data analysis libraries** – pandas, SQLAlchemy for data exploration and migration validation ### **AI-First Cognitive Requirements** - ### **Evaluative judgment** – ability to distinguish plausible AI-generated code from correct code; in high-stakes pricing logic, never self-certify money through AI alone - **Specification precision** – ability to articulate precise intent, edge cases, and constraints before AI generates code; quality of specification determines everything downstream - **Collaborative scepticism** – working productively with AI as a collaborator you direct and challenge, not a tool you wield or an oracle you trust - **Constitution-building mindset** – encoding failures as permanent constraints; maintaining Architecture Decision Records (ADRs) that capture why decisions were made, not just what was decided - **Data-first verification** – the instinct that AI is only as good as the data it works from; verifying data quality at every system boundary before trusting AI outputs ### **Key Responsibilities** - ### Assess and decompose the existing 34-microservice architecture to identify simplification and replacement opportunities - Design and build next-generation backend services using modern Python / TypeScript stacks with LLM-augmented business logic - Own database architecture redesign – migrating from Oracle RDS / MSSQL to modern schemas (PostgreSQL, event stores) that are AI-queryable and analytics-ready - Build data quality frameworks and validation pipelines that ensure AI systems work from reliable, well-structured data - Replace rigid Saga / Orchestrator chains with AI-driven workflow engines and simpler event-driven patterns - Develop LLM-powered solutions to replace hard-coded costing rules, financial calculations, and allocation logic - Operate in the Intent → Generate → Verify → Decide → Document loop, owning decisions on irreversible, money-touching, or outward-facing logic - Build migration pathways (strangler fig, parallel running) to transition from legacy to modern architecture without service disruption - Performance tuning and observability using Grafana, Kibana, and the ELK stack ### **Knowledge Transfer & Key-Person Risk Mitigation** ### This role is responsible for reducing its own bus factor. Concrete expectations: - Pair regularly with the existing DB specialist to transfer backend architecture and data modelling knowledge; document all schema decisions in Markdown-based ADRs - Run fortnightly architecture clinics with the wider team covering Onion Architecture, DDD patterns, and database design for the new platform - Maintain living runbooks so that every critical backend process can be operated or debugged by at least one other team member within 60 days of joining - Contribute to CLAUDE.md-style AI constitutions encoding pricing logic constraints and data quality rules, ensuring institutional knowledge lives in the system, not just in heads - Cross-train at least one frontend developer on backend API design and Python data pipelines within the first 6 months At DXC Technology, we believe strong connections and community are key to our success. 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