Claude Code Experts in Germany

Reporting and controlling with Power BI for a productive ERP system

• Analysis of ERP data and interfaces for use in Power BI dashboards
• Evaluation and migration of existing reports (e.g. Excel) to Power BI
• Development of an access rights concept for selective data access
• Documentation and training on how to use and adapt the Power BI dashboards

Label: Power BI, Excel, SelectLine ERP, Microsoft SQL, SQL Server Management Studio

• Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
• Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
• Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
• Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.

Developed a CPU monitoring application using Spring Boot and React, containerized with Docker Compose, with automated infrastructure provisioning using Terraform on AWS.

Stack: Spring Boot, React, PostgreSQL, REST API, Docker Compose, Terraform, AWS

AI telephone assistant platform

Claude Code, Google AI Studio, Python, LLM / Voice-AI, PostgreSQL

• Conception and hands-on development of an AI-supported telephone assistant platform (voice AI / LLM) – from idea and architecture to MVP/product.
• Built agentic workflows and full automations with Claude Code and Google AI Studio.
• Also delivered AI-supported work in client engagements: used Claude Code for governance documentation, requirement drafts, and automations.

• A recurring problem in my own AI-supported projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but they remain hard to trace and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions as a continuously linked knowledge graph, traceable from the requirement to the architecture decision – queryable for both people and AI agents alike. Technically based on RDF/OWL and its own MCP server.

• Result: MCP daemon running, Docker image automatically published on GHCR, nine hexagonal modules, eleven ADRs (including an open-core licensing model). Requirements engineering and ubiquitous language hexagon active. Publicly available since 07/2026 as a Community Edition under Apache-2.0 (github.com/kogn-io/arknet), together with the Claude Code plugin and the GHCR image; open-core model.

• Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ

Most mid-sized companies already have their numbers. What is missing is the translation: a dashboard with forty tiles does not answer a single question that is actually asked in management.

Analysis

• Evaluation of existing data with Python and SQL
• Checking data quality and methodology before making a statement
• The result is an analysis that leads toward a concrete decision

Preparation

• Reports in Power BI and Tableau
• Interactive calculators and visualizations on the web
• Presentations and specialist texts for customers, sales and the public
• Analysis and communication from one source — that

• Ride-sharing and bus platform, live and working. Backend Spring Boot 4.1 on Java 21, PostgreSQL with Flyway, Testcontainers integration tests. Running in my own AWS account (ECS Fargate, ALB, ECR, IAM least privilege) with CI/CD via GitHub Actions and OIDC federation without static credentials. Development throughout AI-assisted with Claude Code, including my own skills and project-specific memory. Spring Boot · Java 21 · PostgreSQL · Flyway · Docker · AWS ECS/ALB/ECR · CI/CD · GitHub Actions · Claude Code

• Leading UX for a Property Management System, an all-in-one solution for vacation rental agencies and tourism regions, covering marketing and rental of holiday apartments and houses
• UX audits, conception, and implementation of UX strategy with a focus on regulatory, security, and user-centered requirements
• Advising C-level stakeholders on UX strategy and design best practices
• Planning and conducting research with agencies and property owners
• Design system strategy and definition of UX architecture

• Architected the Yatta Integration Layer – a config-driven integration platform on Java 25, Spring Boot 4 (WebFlux), Temporal, gRPC and Kafka, enabling new third-party integrations (e.g. AVS fulfillment) via declarative JSON configs with zero code changes.
• Designed and implemented Tink integration with 0Auth IBAN verification to enhance fraud prevention and account validation workflows with Adyen payByBank.
• Architected and implemented an OpenFGA-based authorization model for centralized management of users, groups, and fine-grained access control in the vendor portal.
• Architected and led delivery of the Yatta API Gateway platform using GraphQL Federation, providing a unified enterprise API layer across distributed microservices with centralized authentication, authorization and request orchestration.
• Replaced NGINX + NLB with Istio service mesh and AWS ALB; rolled out WAF, OAuth (Cognito), IP whitelisting and RBAC across environments.
• Migrated CDC from Confluent Cloud connectors to a self-hosted Kafka Connect + Debezium stack, reducing operational cost by ~80% across multiple environments.
• Implemented the Transactional Outbox pattern with Debezium for reliable, exactly-once event publishing to Kafka with Avro and Schema Registry.
• Migrated dunning/payment-recovery workflows from Airflow to Temporal, achieving 99.9% reliability for settlement handling.
• Optimised Apache Airflow with deferrable sensors to handle 1000+ concurrent DAG runs without scaling the worker pool.
• Refactored a monolithic Terraform codebase into 3 modular projects, cutting deployment time by ~45%.
• Stood up full observability with OpenTelemetry, Tempo, Prometheus and Loki; automated dev/staging/prod with ArgoCD, Image Updater and Helm.
• Collaborated with product, operations and engineering stakeholders to define scalable platform architecture and integration standards aligned with long-term business and operational goals.

• Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
• Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
• Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
• Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.

Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy

• Training the AI models, evaluating images and texts for UI/UX, turning the provided data into insights via OpenAI Feather as part of the machine learning workflow

Technologies: OpenAI Feather

Designing AI-native digital products by combining product strategy, UX research, interaction design, software engineering, and modern AI workflows. Leading projects from discovery to implementation while integrating AI throughout the entire product development lifecycle.

Key responsibilities

• → Product discovery, stakeholder workshops, Jobs-to-be-Done and user research
• → User journey mapping, information architecture and interaction design
• → Wireframes, high-fidelity UI, prototypes and scalable design systems in Figma and Penpot
• → AI-assisted interface generation and rapid concept exploration using Figma AI, Figma Make and generative design workflows
• → Design-to-code workflows with AI-supported frontend generation and engineering collaboration
• → Building accessible interfaces following WCAG 2.2 and enterprise design standards
• → Usability testing, iterative validation and KPI-driven product optimization
• → Development of AI knowledge systems, MCP-powered design workflows and human-in-the-loop review processes
• → Close collaboration with engineering teams to ensure production-ready implementation

• Leading end-to-end UX for two AI SaaS products in closed beta, including LLM-interaction design, prompt-UX, and human-in-the-loop patterns with commercial distribution signed for launch in Q3 2026
• Built a self-built LLM reframing and RAG-correction pipeline powering multi-profile CV and case-study generation in production use
• Shipping real code alongside research, including Three.js/GLSL portfolio work, Figma-API tooling, and a Chrome MV3 extension for session-sync automation

• Built and validated SupportPiloten, an AI-powered content operations service; won first paying pilot customers
• Tested agentic workflows with Claude Code and Codex for analysis, research, documentation, and prototyping
• Further developed own AI product; training: AI governance, AI compliance, and the EU AI Act (ongoing)