Senior Platform Engineer

Job Description:

• 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

• 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

• 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

• 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

• 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.

Recruitment fraud is a scheme in which fictitious job opportunities are offered to job seekers typically through online services, such as false websites, or through unsolicited emails claiming to be from the company. These emails may request recipients to provide personal information or to make payments as part of their illegitimate recruiting process. DXC does not make offers of employment via social media networks and DXC never asks for any money or payments from applicants at any point in the recruitment process, nor ask a job seeker to purchase IT or other equipment on our behalf. More information on employment scams is available here.