WPS MANAGEMENT GMBH Logo

WPS MANAGEMENT GMBH

100% Remote / Paderborn

CTO at WPS MANAGEMENT // wescale

Chief Technology Officer

September 2023 - June 2025
1 year 10 months
Full-time
Product work
100% Remote / Paderborn

Relevance

Why this case matters

The case shows how problem understanding, responsibility, decisions, and implementation came together in this context.

Impact

Strengthened a delivery system across 15 teams, scaled 100+ developers remotely, and replaced activity metrics with flow-based execution.

What carries forward today

Established delivery rules, golden paths, and guardrails for a large-scale engineering system under real operational pressure; in parallel, early and continuous LLM use plus my own role-based agent workflows in n8n, complemented by experiments with locally hosted models.

Remote scalingCTO transformationAgile coachingProduct ownershipYou Build It You Run ItFlow metricsCloud-nativePCI DSS 4.0CypressLLM workflowsAgent workflows (n8n)Local LLMsMCPMicrosoft 365 Copilot

Defensible proof

15 teams

led cross-functionally

100+ devs

scaled remotely

Flow

shorter, more reliable deployment cycles

Where this experience creates value

  • Hiring
  • Head of Engineering
  • CTO-level
  • Fractional transformation

Case context

Overview

WPS MANAGEMENT needed remote product development at 100+ developer scale to stay steerable without adding another coordination layer. I led 15 cross-functional Scrum teams with FaST Agile, flow signals (cycle time, lead time), and cloud-native architecture on GCP/Azure in a you build it, you run it model. I also covered interim Product Owner responsibility for one team. The setup included internationally distributed freelancers, including Turkey and Cyprus, plus technical alignment with team leads around GKE-adjacent platform responsibility.

The work shifted attention from output steering to better delivery frequency, higher team autonomy, and clearer golden paths. The important part is not the framework. It is that product development can stay steerable even in large remote scaling.

In parallel, I brought AI into daily work early: continuous productive LLM use since February 2023 (starting with GPT-3.5) plus my own role-based assistant and agent workflows in n8n (Assistant node with memory) for recurring engineering, analysis, and delivery tasks. On top of that came MCP connections and Microsoft 365 Copilot (at its core an OpenAI model with Microsoft Graph grounding) in the work environment, plus a few small tests with GitHub Copilot. I also experimented with locally hosted models to weigh privacy, cost, and control trade-offs in practice. This was not tool hype but concrete workload relief with clear boundaries.

Responsibility

Activities

  • Remote leadership of 15 Scrum teams with 100+ developers without adding another coordination layer
  • Led the Agile Coaches team and evolved product ownership, flow, and delivery practices
  • Covered interim Product Owner responsibility for one team from problem definition and prioritization through delivery and operations
  • FaST Agile implementation: flow signals, team autonomy, golden paths
  • Cloud-native transformation (GCP/Azure): Kubernetes, CI/CD, infrastructure-as-code
  • GitLab/GitLab CI and GKE context in the platform and delivery landscape; operational GKE responsibility sat with the responsible team lead
  • Microservices architecture: Spring Boot, Docker, Kubernetes, service mesh
  • DevOps culture: "You build it, you run it", Cypress tests, automated testing, monitoring
  • Compliance governance: first PCI DSS 4.0 draft and recurring alignment with Visa stakeholders
  • Distributed organization: integrated internationally distributed freelancers and external capacity into shared quality, review, and delivery standards
  • Product thinking: Outcome-focused development, user value measurement
  • Team enablement: shared ownership and cross-functional collaboration
  • AI in my own daily work (personal productivity and evaluation, not a team-wide rollout): built my own role-based assistant and agent workflows in n8n (Assistant node with memory) and used LLMs productively every day from February 2023 for my engineering, analysis, and delivery tasks; complemented by MCP connections, Microsoft 365 Copilot, small GitHub Copilot tests, and experiments with locally hosted models to assess privacy, cost, and control

Operating mode

Methodology

  • FaST Agile: flow signals, team autonomy, outcome focus
  • Scrum: Cross-functional teams, sprint planning, retrospectives
  • DevOps: Continuous integration/deployment, Cypress tests, infrastructure-as-code
  • Product thinking: User value, impact measurement, iterative development
  • Team enablement: shared ownership and golden paths
  • AI as a tool: LLM and agent workflows in my own work where they measurably speed up delivery and analysis, with deliberate attention to privacy, cost, and control (including locally hosted models)

Technical context

Technology stack

The tools are not the point by themselves. What matters is which system layers had to work together.

9Areas
52Technologies

DevOps

12
GitMavenKubernetesDockerGCPAzureTerraformAnsiblePrometheusGrafanaGKEDevSecOps

Databases & Storage

4
Google Guava CachePostgreSQLMongoDBElasticsearch

Messaging & Event Streaming

1
Google Pub/Sub

Frontend

4
Tailwind CSSTypeScriptJavaScriptVue.js

Tools

9
ViteGitLabCypressJiraConfluenceSlackMicrosoft Teamsn8nMCP

Practices

7
Test-Driven DevelopmentAgile TestingAgile CoachingProduct OwnershipPCI DSS 4.0Visa Stakeholder ManagementInternational Distributed Teams

Backend

8
JavaSpring BootSpring CloudSpring DataNode.jsPythonFastAPIREST APIs

CI/CD & Delivery Pipelines

3
GitLab CIJenkinsSonarQube

Data & AI

4
LLM WorkflowsLLM-assisted DevelopmentLocal LLMsMicrosoft 365 Copilot

Next step

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