Data Modeling Experts in Munich

• Leading enterprise BI transformation across Power BI & Microsoft Fabric, delivering scalable data models, automated reporting, and high-performance analytics solutions for commercial and operational leadership.
• Building and optimizing Power BI Dataflows, Fabric Lakehouse datasets, semantic models, and automated reporting pipelines to improve data scalability, governance, and reporting performance.
• Driving dashboard modernization and KPI governance by translating complex business requirements into executive-level insights, interactive visualizations, and decision-ready analytics.
• Designing end-to-end Microsoft Fabric architectures integrating data ingestion, transformation, virtualization, and enterprise reporting across cross-functional business domains with SAP BW to Qlik to Power BI migration.
• Delivering AI-enabled reporting capabilities, threshold-based alerting, and automation frameworks within the Power BI ecosystem to accelerate business decision-making.
• Partnering with commercial leadership, analytics teams, and IT stakeholders to standardize KPIs, optimize BI strategy, and deliver scalable, business-critical reporting solutions.
• Recognized for combining strong stakeholder leadership, technical architecture expertise, and business-driven analytics to deliver impactful enterprise BI transformation initiatives.

• Clarification of project objectives, scope and the target business vision for building the AI-based knowledge database.
• Derivation of the initial architecture and implementation strategy for the Zasterbot chatbot, including separation of the MVP and further development stages.
• Development of a target business vision for building a structured knowledge database and integrating a future chatbot.
• Derivation and prioritization of use cases for information retrieval and provision by the chatbot.
• Modeling of data structures and flows for the effective organization of the knowledge database on the Base44 platform.
• Design and implementation of data models for storing and linking relevant information.
• Development of processes for extracting, analyzing and preparing raw data for the knowledge database.
• Ensuring data consistency and quality as the basis for the future chatbot.
• Planning the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for precise and context-specific answers.
• Implementation of functions for analyzing and displaying data from the knowledge database.
• Use of the Base44 platform with JSON Schema-based entities and a flexible permission concept.
• Implementation of Deno functions for backend logic, event processing and connecting external APIs.
• Integration of OpenAI services for initial data analysis.

Topics:

• Further development of the CRM system NKAM and technical separation of “direct” and “indirect” sales
• Redesign of the discount management area and outsourcing to another system.
• Designing numerous improvements on the wholesale side to reduce manual effort down to the level of user stories.

Unified ITSM framework

• Definition of a company-wide ITSM target picture
• Introduction of a uniform service structure across all business units

SLA and OLA management

• Building a standardized SLA framework
• Definition of service classes (Business Critical, Standard, Low Priority)
• Introduction of OLAs between internal teams
• Building meaningful SLA reporting
• Definition of KPI and service dashboards for business units

Service portfolio management

• Definition of service descriptions
• If needed, preparing possible cost and service billing

Ticketing & processes

• Incident management
• Uniform ticket categories
• Standardized prioritization
• Escalation matrix
• Automations
• Self-service optimization

Request fulfillment

• Service catalog across all business units
• Approval workflows

Problem management

• Introduction of root cause analysis
• Known error database
• Problem review process

Complete asset management concept

• Hardware lifecycle management
• Software lifecycle management
• Leasing lifecycle
• Mobile device lifecycle
• Monitor lifecycle
• Phone lifecycle

Processes

• Procurement
• Goods receipt
• Inventory
• Assignment
• Return
• Disposal
• Leasing return Goal: single source of truth for all assets

CMDB design

• Definition of all configuration items:
• Workplace
• Notebooks
• Monitors
• Mobile phones
• Printers

Infrastructure

• Servers
• Firewalls
• Switches
• WLAN
• Storage
• Backup systems

Cloud

• Azure resources
• Microsoft 365
• SaaS services

Relationships

• User ↔ Asset
• Asset ↔ Service
• Service ↔ Infrastructure
• Location ↔ Asset
• Goal: make all service dependencies visible

Software asset & license management

• License management concept
• License balancing
• Compliance reporting
• Microsoft license management
• Adobe license management
• SaaS management
• Contract management
• Renewal management

Interfaces & automation Existing systems

• Workday
• Joiner
• Mover
• Leaver

TESMA

• Leasing data
• Contract data

Matrix42

• Asset synchronization
• User synchronization

Active Directory / Entra ID

• User management

Microsoft 365

• License assignment
• Group management

Dormakaba

• Access processes

• Lifecycle services

Monitoring platforms

• PRTG
• Palo Alto
• Cisco

Reporting & KPI framework

• Definition of a management dashboard
• KPIs
• Ticket volume
• SLA fulfillment
• MTTR
• First resolution rate
• Asset accuracy
• License compliance
• Change success rate
• Service availability
• Degree of automation

Network redesign support

• Governance
• Support of the network redesign from an ITSM point of view
• Definition of affected services
• Change management structure
• Communication concept

CMDB integration

• Recording of all network components
• Service mapping
• Dependency analysis

Validation of documentation and knowledge base articles

• Network documentation
• Operations documentation
• Standard changes

Monitoring & event management

• Target picture
• Central monitoring concept
• Event management process
• Alerting strategy
• Escalation model

Systems

• Cisco

• Palo Alto

• Fortinet

• Rubrik

• Veeam

• Matrix42

• Azure

• Microsoft 365 Automation

• Ticket creation from monitoring

• Escalations

• Standard actions

Audit, compliance & information security

• ISO 27001 consulting
• TISAX consulting
• NIS2 preparation - consulting
• Audit-ready processes
• Documentation structure
• Evidence tracking in Matrix42

Roadmap

• 12-month roadmap
• Prioritization of all measures
• Quick wins
• Medium-term projects
• Long-term target picture
• Documentation

• Led enterprise reporting modernization by migrating legacy SSRS reporting solutions to Power BI, supporting 500+ business users while ensuring full GDPR/DSGVO compliance.
• Designed and optimized Power BI and Microsoft Fabric semantic models using star schema, dimensional modeling, advanced DAX, and performance optimization techniques, reducing query latency by 25%.
• Delivered 20+ executive and operational dashboards featuring KPI scorecards, drill-through, bookmarks, and row-level security, improving reporting efficiency by 20%.
• Enabled self-service analytics through governed Power BI datasets, dataflows, and gateway architecture, increasing business-led reporting adoption by 35%.
• Configured an incremental refresh policy and query folding for a 50+ million row sales dataset, reducing daily report refresh times by 85%.
• Deployed automated ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, and Snowflake, reducing reporting delivery timelines by 40% through workflow automation.
• Spearheaded Microsoft Fabric analytics modernization initiatives including lakehouse architecture, OneLake integration, and centralized data platform development, reducing data latency from 2 hours to 20 minutes.
• Translated business requirements from 15+ stakeholders into scalable Power BI semantic models and dashboards, improving reporting consistency and reducing ad-hoc reporting requests by 25%.
• Applied Microsoft Copilot and generative AI tools to accelerate SQL development, DAX authoring, technical documentation, and testing activities, reducing development effort by approximately 15 hours per week.

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

• Defined the vision, strategy, and roadmap of AI-powered analytics products, ensuring they met the business needs of Sales, Marketing, Finance, and executive teams across Europe.
• Collected business requirements, prioritized AI product features, and led Agile development of forecasting and analytics solutions. Defined product specifications, user stories, and acceptance criteria to ensure successful delivery.
• Collaborated with business stakeholders, Product Owners, data scientists, ML engineers and software engineers to transform AI models into scalable business products and integrate AI insights into operational workflows.
• Designed and implemented Generative AI solutions leveraging Large Language Models (LLMs) to automate reporting and enable natural-language querying of enterprise data, reducing manual effort by approximately 30%.
• Defined product goals and success metrics, tracked product performance and user adoption, and continuously improved the product based on user feedback and business results.
• Established data governance, master data quality and reporting standards across SQL, BigQuery and Power BI environments to ensure reliable, secure and scalable analytics.

struppilog.com – Digital health record for pets / MVP → Full Product

Design, development, and full further development of a digital health platform for pets – from my own MVP development to a fully built and production-ready platform.

Independent concept and development of the MVP Development of the full application with Flutter/Dart and Firebase Expansion of the MVP into a full digital health record with health data, findings, allergies, medications, documents, and emergency data Development of user registration, authentication, roles, data models, and secure user interactions Implementation of QR-code-based data exchange and digital interaction features Development of a multilingual, responsive web application Integration of AI-supported features and AI/agentic workflows Development and continuous improvement of product logic, UX/UI, and technical architecture Building and expanding a scalable cloud-based solution with Firebase Integration and further development of APIs and external services Use of AI-native / agentic engineering to speed up development, testing, debugging, and product iteration Independent implementation of all other features and technical extensions Continuous further development of the MVP into a full digital product

Impact: The MVP I built myself was continuously developed technically and functionally into a broad, production-ready platform – including frontend, backend, data model, authentication, UX/UI, APIs, cloud infrastructure, and ongoing product development.

• Built a Python pipeline that converts lectionary web content into structured daily JSON, applying liturgical-calendar rules for accurate weekday and Sunday coverage.
• Shipped a read-only FastAPI REST API with shared Pydantic models and delivered a Kirundi-first web client for browser and mobile use.
• Owned the data layer and backend architecture, collaborating closely on system architecture and interfaces while automating refreshes with GitHub Actions and validating the ETL with pytest.
• Impact: Created a reliable, API-driven source of truth for daily Bible readings in Kirundi, enabling consistent access to previously unstructured content.

• Applied modern JavaScript features, including ES6+ syntax, destructuring, modules, array methods, and asynchronous programming, to develop efficient and readable frontend code.
• Implemented frontend integration patterns for PHP and Laravel backend services, connecting React interfaces to REST APIs and handling structured JSON data.
• Developed interactive, functional component-based frontend applications using React Hooks, JavaScript, and TypeScript, alongside backend applications and RESTful APIs using Python, Django, Flask, and FastAPI, focusing on reusable components, modular architecture, maintainability, and efficient data processing.
• Maintained a dynamic portfolio platform using PHP, Laravel, Livewire, and MySQL, implementing end-to-end functionality across frontend interfaces, backend services, and database operations.
• Validated server-side request handling, routing, middleware, and form validation using Laravel features to support reliable application workflows and consistent data processing.
• Designed and managed relational database structures using MySQL and Laravel Eloquent ORM, implementing data models, relationships, database migrations, and efficient queries for content management.
• AI-powered applications integrating Large Language Models (LLMs) through external APIs and locally hosted models, exploring practical use cases for conversational AI, intelligent automation, and AI-assisted workflows.
• Built local AI applications and developer tools for running, configuring, and interacting with locally hosted language models, gaining experience with local inference, model integration, and privacy-conscious AI workflows.
• Explored Retrieval-Augmented Generation (RAG) approaches to combine language models with external knowledge sources, enabling context-aware responses based on relevant application data and retrieved information.
• Integrated AI services with backend APIs and application components, developing reusable modules for model communication, response processing, error handling, and data exchange.

• Migration of existing data jobs from Cognos Data Manager to Tibco/IBI Datamigrator
• Migration data jobs parametrisation for dynamic runs
• Optimisation and cutting-back
• Regression tests
• Knowledge transfer and documentation

• It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
• GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
• Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
• CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
• Development of feature store and data synchronization between environments
• Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
• Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
• Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
• Development of Copilot Agent Skills

Tasks

• Further development of a new ERP system with Blazor
• Generation of e-invoices in the XRechnung and Zugferd formats
• UI/UX design Client-server system
• Windows 11 Technology
• Microsoft .NET 9, Git, Azure DevOps, ASP.NET, MSSQL, Blazor Programming languages
• C#, MVVM Development tools
• Microsoft Visual Studio .NET 2022 Database
• MSSQL Industry
• Other

• Develop data products as part of a Data Mesh / Medallion architecture on Databricks and Azure Synapse.
• Build Power BI dashboards using consolidated data from multiple source systems (sales, service, contracts, inventory data).
• Define KPIs and reporting standards in close collaboration with business stakeholders.