Senior Platform Engineer

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

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