Principal Systems Engineer

Om jobbet

Job Description:

1. Develop and maintain the organisation's overarching IT systems strategy - conducting structured analyses of current and future business requirements, evaluating technology alternatives through cost-benefit and feasibility studies, and producing strategic roadmaps that ensure the IT infrastructure effectively supports product and business objectives across native mobile platforms (iOS, Android), on-device AI, and cloud services.

2. Analyse, model, and specify end-to-end system architectures spanning cross-platform mobile applications, real-time synchronisation infrastructure, local-first data persistence layers, and on-device machine learning pipelines - producing architectural blueprints, component interaction diagrams, data flow models, and interface specifications using systems design methodologies and architectural modelling tools (Enterprise Architect, Lucidchart, ArchiMate, Miro).

3. Apply industrial systems engineering and operations research principles - including Queueing Theory, process simulation, throughput analysis, and Six Sigma - to analyse and optimise the software development lifecycle (SDLC), improve compute resource allocation, reduce cycle times, and systematically identify and eliminate process inefficiencies across engineering, testing, and deployment workflows.

4. Produce functional and technical specifications for native iOS (Swift, SwiftUI, SwiftData/Core Data, Swift Concurrency with actors, Core ML/MLX/Create ML) and Android (Kotlin, Jetpack Compose, Room, Coroutines/Flow, MediaPipe/TensorFlow Lite/ML Kit) systems for use by software development teams - defining architectural patterns, persistence strategies, concurrency models, and modularity frameworks (Swift Package Manager, Gradle multi-module with Version Catalogs) that enable rapid, high-quality delivery.

5. Analyse and architect polyglot persistence and real-time data synchronisation solutions - specifying local-first offline-capable data architectures with conflict resolution strategies, real-time sync via WebSockets and Firebase Realtime Database/Firestore, and backend persistence layers (PostgreSQL, MongoDB, Redis) - producing data models and ensuring consistent, low-latency access patterns across mobile and server systems.

6. Evaluate, select, and specify the integration of AI-powered tooling across the engineering workflow - from coding assistants (Claude Code, Codex, GitHub Copilot) and AI-driven code review, to automated test generation and intelligent CI/CD pipeline optimisation - conducting cost-benefit analyses, defining adoption frameworks, and measuring productivity impact to ensure the organisation's tools and processes operate at maximum efficiency.

7. Specify cloud infrastructure architecture using Infrastructure as Code (Terraform, GCP, Pulumi), containerised deployment strategies (Docker, Kubernetes/GKE), and automated CI/CD pipelines (GitHub Actions, CircleCI) with integrated security scanning (Snyk, SonarQube) - producing infrastructure design documents, capacity models, and system reliability requirements to ensure environments are elastic, self-healing, and aligned with security-by-design principles.

8. Analyse data flows and specify compliance architecture for EU data protection regulations (GDPR) and applicable Data Privacy Acts - designing Zero Trust Architecture frameworks, specifying end-to-end encryption requirements (TLS/SSL) across all system boundaries, conducting privacy impact assessments, and producing compliance documentation for use by engineering and legal teams.

9. Conduct systematic architecture reviews using structured risk assessment and tradeoff analysis - identifying and evaluating technical debt, producing prioritised refactoring strategies, and surfacing architectural risks early with actionable recommendations that align engineering investment with business objectives and long-term system sustainability.

10. Define and evolve system-wide engineering standards, quality frameworks, and process governance - establishing architecture review protocols, specifying system monitoring and observability requirements (Datadog, Prometheus, Grafana), defining development workflow standards, and producing long-term architectural roadmaps that enable the organisation to scale rapidly while maintaining system reliability, performance, and regulatory compliance.