PostgreSQL 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

AI Readiness - Part 3: Creating an e-book on the use of AI in software testing

• Designed and created an e-book on the sensible and safe use of AI in software testing from the perspective of an experienced test expert.
• Used generative AI (ChatGPT, Perplexity Pro) as a sparring partner for structuring, wording suggestions and creating variations, with all content finally reviewed and approved using my own expertise.
• Developed practical insights into the opportunities and risks of AI in testing, with a special focus on collaboration between testers and AI instead of “tester vs. AI”.
• Title: “11 starke Gründe: Dream-Team statt Rivalen - Tester und KI sind gemeinsam unschlagbar”
• Subtitle: “Wie Mensch und Maschine im Softwaretest das perfekte Team bilden – und warum reines KI-Testing riskant ist.”
• Planned use of the e-book as a basis for workshops, customer meetings or internal training.
• Technology: Generative AI, ChatGPT, Perplexity Pro, Microsoft Word, Adobe Acrobat, many years of hands-on professional testing experience, common sense.

• Extension of a Swing-based UI for recording Security Restricted Articles during baggage screening.
• Introduction of a new workflow for recording Deboarding Events.
• Extension of the reporting system for Security Restricted Articles and Deboarding Events.
• Implementation using JBoss Wildfly, PostgreSQL, spring-boot 3.x, Java 8/21, Swing, JSF, JAX-RS/Resteasy, Docker, Maven, Kiro.

AI phone assistant platform

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

• Designed and hands-on developed an AI-powered phone assistant platform (Voice-AI / LLM)—from idea and architecture through to MVP/product.
• Built agentic workflows and end-to-end automations with Claude Code and Google AI Studio.
• Also supported AI-powered delivery in client engagements: using Claude Code for governance documentation, requirements drafts, and automations.

Project – Evaluation and development of databases for a new product line for a measuring equipment manufacturer

• Current-state analysis
• Requirements analysis of the data storage layer
• Writing benchmark tests
• Design of the new database schema
• Database migrations
• Development of audit logging and Part11
• Creation of backup and recovery concepts
• Design of the persistence layer API
• Writing unit tests
• Creating requirements in Azure

Tools: C#, Visual Studio 2022, Azure DevOps, SonarQube, Git, RestSharp, WebServer, PostgreSQL, MS SQL, SQLite, MinIO, Podman, Docker, DataGrip, SQL Management Studio, HeidiDB, pgAdmin

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

Led the end-to-end integration and migration architecture for decommissioning SAP IS-U market communication and transitioning to Salesforce Marketing Cloud (SFMC) with E.ON IT Services — covering regulated EDIFACT market messaging, a three-phase data migration (transfer of market message inventory after cutover), redesign of market communication processes, and full GDPR and German energy-market compliance.

• Designed the three-tier target architecture (SAP IS-U → NScale B2B gateway → SFMC) processing 50,000–100,000 EDIFACT messages daily within sub-5-minute SLAs.
• Architected the three-phase data migration: 36 months of historical data, zero-loss cutover of in-flight market messages, and near-real-time CDC synchronization.
• Delivered secure, BDEW/GDPR-compliant integration (AS2/OFTP2, RFC/IDoc, REST APIs, OAuth 2.0, Azure AD, AWS S3) achieving >99.5% uptime, <0.1% migration discrepancy, and RTO <4 hours.
• Applied BPMN to model and optimize the market communication processes.

Technologies: SAP IS-U, NScale, Salesforce Marketing Cloud, EDIFACT, OAuth 2.0, Azure AD, AWS S3, BPMN, Jira, Confluence, Draw.io

• Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
• Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
• Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
• Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
• Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
• Built a business layer architecture for reporting, data warehousing, and reusable data products
• Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
• Worked closely with business departments to analyze business requirements and translate them into scalable data architectures

Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4

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.

Development of AI and traditional models to analyze medical and patient data, including medication analyses, diagnosis analyses, forecasts, procedure analyses, dosage analyses, comorbidity analyses, prescription analyses, patient potential analyses, referral profile analyses, and prescription analyses. Analyses in the field of Real World Evidence.

Healthcare IT, pharmaceutical industry

Tasks, responsibilities:

• Gathering customer requirements
• Planning the subproject
• Designing and defining KPIs
• Designing and developing models and visualizations of results using customer dashboards
• Developing and implementing data warehouse adaptations
• Deriving recommendations for action

Methods, technologies:

• Simulation
• Artificial intelligence
• Python
• R
• SQL
• Microsoft Power BI
• Amazon Web Services
• Elasticsearch
• PostgreSQL
• Databricks
• Multivariate statistics

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.