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. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing, productivity, individual work styles, and life circumstances. We’re committed to fostering an inclusive environment where everyone can thrive.

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