DXC Technology

Senior Platform Engineer

Vaga remota de Platform Engineering com fit claro de localização do candidato.

Publicada13 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

Locais aceitos para candidatos

México

Resumo da vaga

Conteúdo da vaga extraído em seções para revisão mais rápida.

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

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