GitHub Experts

I designed and built a self-service AWS platform for Data Science teams with little or no infrastructure-as-code experience. A team writes about 10 lines of Python and gets a deployable stack with full SDLC release cycle that already follows the governance rules. Networking, encryption, IAM and tagging are decided by the platform, not by each team.

New teams or AWS accounts are onboarded with one pull request. Waste prevention and cost control is built into the defaults: Data scientists don't need infrastructure skills.

• Construct library (AWS CDK, Python). Built a versioned catalog of about 20 hardened constructs for being used in data-science projects with ML context
• Governance as code
• Multi-account, multi-tenant
• Golden-path scaffolding
• CI/CD and supply chain. Built reusable GitHub Actions workflows
• Release engineering
• QA with e2e, contract testing and unit test coverage with each release candidate

• Developed a pilot system to control and monitor multiple GoPro cameras
• Implemented start, stop, status, media, download and diagnostic functions via COHN endpoints
• Built camera detection, individual and group control, and stable session/KeepAlive logic
• Set up a download pipeline with a queue, retry handling and temporary files
• Ensured consistent session, folder and file naming for multicamera recordings
• Integrated provisioning, certificates and network operations

Working Environment

• Insurance / Claims Management (Property, Liability, Automotive)
• REST-based ClaimAPI with Guidewire ClaimCenter

Scope

• Further development of the internal REST-based ClaimAPI with Guidewire ClaimCenter as the backend and various portals/front-end offices as clients, and with the following objectives: + increased AI-supported claims processing outside of Guidewire ClaimCenter + and support for all lines (property, liability, automotive).
• Further development of automated claims processing

Activities

• Interfacing IT & Business
• Design activities with documentation in Confluence, Jira: + Requirements engineering (functional and non-functional) + Design of REST endpoints and coordination with interface partners (esp. AI-colleagues) + Identification, design and evaluation of alternative solutions (moving away from a monolithic approach toward fine-grained endpoints, with code reuse across all lines if possible) + Creation / refinement of product backlog items in Jira, i.e. epics, user stories and tasks, incl. alignment with developers & POs during (pre-)refinement sessions + Using AXA Secure GPT when creating / refining user stories (description, acceptance criteria) and designing REST endpoints + Estimation and prioritization of user stories and tasks during Sprint Planning sessions + Coordination and support during the implementation of Product Backlog items + Driving decision making with preparation and presentation of decision-making documents
• Quality assurance: + Test management, including test case creation using Xray in Jira + Test execution using Bruno as REST client and in Guidewire ClaimCenter + Submission of test artifacts for PO approval + Analysis of production bugs

Methods Big Room Planning (aka PI-Planning in SAFe), Scrum, BPMN, EPK, UML, ERM, DDD

Tools / Tech Stack Teams, Confluence, Jira with Xray, OpenAPI/Swagger, REST, JSON, XML, MS Office, Bruno, GitHub repository (reading), Guidewire ClaimCenter, AXA Secure GPT (AXA's in-house GenAI solution based on Azure OpenAI)

• Developed browser based platform for kubernetes no-code deployment and cluster management
• Developed CLI in Typescript to deploy the resources in the cluster without leaving the browser UI.
• Implemented DevSecOps pipelines: image scanning, SBOM, policy enforcement, supply-chain security and used Kyverno. Implemented IAM integration for the command line utility tool.
• Designed role & permission models for Keycloak, oAuth/OIDC, social login flows.
• Used LLM to convert user intent into the diagram.
• Worked on integration with multiple Sovereign Cloud like stackIt, Hetzner, CIVO, UpCloud plus public clouds like AWS, GCP, Azure
• The technology stack includes Java, SpringBoot, Kubernetes, OpenAI, Kubernetes multi-tenancy using vCluster, Karpenter, RBAC for CLI, Helm, React

TOURISM

colle-il-diaccino.de · Holiday Accommodation

• Designed and developed the website: interactive site map, accommodation comparison, and image galleries.
• Implemented German, English, and Italian language versions. Set up inquiry forms with input validation and spam protection, language-specific SEO metadata, and automated tests.

Stack: Next.js, React, TypeScript, Tailwind, next-intl, React Hook Form, Zod, Resend, Playwright, Vitest

• Built a data integration platform for high-, medium- and low-voltage assets to enable contextual analysis of time series with master data from SCADA control systems (IEC 60870 104), INIS and SAP.
• Responsible for the architecture and implementation of the solution, and acted as a sparring partner for the Product Owner.
• Technologies used: Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter and various API gateways.

• Create and lead roadmap forecasting of support opportunities, including colleague training, process design and improvement, and new hire onboarding.
• Collaborate seamlessly with cross-functional teams including Professional Services, Support, Product, Sales, and Solution Engineering.
• Manage business and financial KPIs in alignment with overall business objectives.
• Industrial IT & Data Integration: Implement, configure, and integrate OT/IT systems and data flows, connecting control-layer sources (DCS, PLC, SCADA) to historians, data platforms, and enterprise systems through APIs and standard interfaces.
• AVEVA PI & Historian: Configure, maintain, and extend AVEVA PI (PI Data Archive, Asset Framework, interfaces/connectors) to capture, contextualize, and serve real-time and historical operational data.
• Alarm Management: Support and optimize alarm management systems – e.g. Honeywell Alarm Management – including alarm rationalization, classification, and the analysis of alarm performance, critical alarms, and alarm floods.
• Connectivity & Protocols: Set up and troubleshoot industrial connectivity using OPC UA and OPC A&E, including the configuration of tunnelers / wrappers to bridge OT data sources securely and reliably.
• Production Data Flow: Ensure seamless, secure, and well-structured data flow across the industrial IT landscape, transforming raw operational data into meaningful information for operations and decision-making.
• System Support & Troubleshooting: Provide ongoing support for OT/IT applications, troubleshooting issues across networks, software, and data integrations to keep systems running efficiently.
• Knowledge Management & Best Practices: Contribute to internal documentation, best practices, and troubleshooting guides, and help continuously improve how we deliver industrial IT and data solutions.

Tools & Software: Aveva PI, Salesforce, Active Directory, Microsoft Dynamics 365, Jira, Microsoft, Scrum, DevOps, Docker, CI/CD, Jenkins, GitHub.

Product:

• Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
• Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
• Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
• Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
• Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.

Agent Orchestration & RAG Systems

• Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
• Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.

Further Development of the Integrated Capacity Management Platform InKa

Rail Infrastructure

• Power BI shared datasets and reports via DirectQuery on Amazon Redshift (timetable, construction and capacity data)
• Automated report testing with Playwright in Docker containers, version control with GitLab
• Concept and implementation of user communication (information page, changelog, known bugs)

Initial Situation:

The integrated capacity management platform (InKa) is a monitoring and analysis platform for data from timetable and capacity management: customer orders, infrastructure, timetable operations, construction, as well as route capacity and width. The data is stored and linked in a structured data warehouse. This makes it possible to create complex KPIs across different source systems and display current and future route utilization as well as construction activity across the network. The goal is to improve strategic, medium-term and intra-year planning and provide users with easy access to capacity management analyses.

Measures:

• Enablement: Introduction of Power BI with training and workshops for development teams.
• Consulting & Design: Problem analysis, concepts and solution consulting for reporting requirements; implementation of reporting solution plans.
• Data Model: Development of shared datasets with DirectQuery on Amazon Redshift and import tables; DAX measures for utilization and capacity KPIs.
• Development: Data analysis, data transformation and visualization in Power BI; extension and migration of business applications to Power BI; AI-supported development in VS Code with GitHub Copilot.
• Test Automation: Automated testing of Power BI reports with Playwright; setup of the test environment in Docker containers.
• User Communication: Concept and implementation of a central information page with hot news, changelog and known bugs, access from reports with report filters, updates via Power Automate.
• Deployment: Management of Power BI Deployment Pipelines across the DEV, TEST and PROD environments.
• Agile Working: Work in an agile team according to SAFe (Scaled Agile Framework); refinement of user stories and features including effort estimates in Jira; version control with Git and GitLab.
• Quality & Documentation: Reviews for quality assurance, error analysis and resolution, technical documentation in AsciiDoc and Confluence.

Technologies: Power BI, DAX, Power Query, DirectQuery, Amazon Redshift, Git / GitLab, Deployment Pipelines, Playwright, Docker, AsciiDoc, Power Automate, Jira, Confluence, VS Code with GitHub Copilot, SAFe

Results:

• Capacity management KPIs from multiple source systems combined on one platform
• Transparency regarding current and future route utilization and construction activity across the network
• Easy access to analyses for users
• Automated regression testing of Power BI reports
• User communication implemented: central information page with hot news, changelog and known bugs, directly accessible from the reports

• Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility

• Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture

• Pipeline orchestration with Azure Data Factory

• Improved and automated the development and deployment process by integrating Git and GitHub Actions

• Technical leadership of the Data Engineering team (3 employees)

• Led workshops to optimize and stabilize the data platform and development process

• Collected and prioritized new requests and maintained the product backlog

• Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural

Business development and professional exchange

• Developed a consulting offer for digital transformation, AI adoption, and Operating Models; built industry and partner relationships.

Stabilization of a critical logistics outsourcing setup in the context of an SAP S/4HANA transformation. The focus was on restoring a reliable operational fact base between the client, logistics service provider, international supplier plants and the SAP/IT organization.

• Coordination between the client, global logistics service provider, international supplier plants, SAP/IT teams, Quality Management and the operational shop floor.
• Management, monitoring and operational correction of Inbound Deliveries; integration of Outbound Delivery processes.
• Analysis of interface, posting, batch and master data errors between SAP S/4HANA, logistics service provider, supplier plants and internal business functions.
• Significant reduction of critical inventory differences between the client and logistics service provider.
• Improved operational management capability in the logistics environment and creation of a common fact base.
• Reduction of recurring error patterns in the areas of Inbound Deliveries, inventory, interfaces, batch/posting logic and service provider processes.

• Standardization and further development
• Application monitoring
• Incident handling
• Change management
• Interface and integration operations
• Testing, acceptance and transition to operations