Microsoft Fabric Experts in Germany

• 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.

Tools: Fabric, AWS, dbt, Power BI, SQL, DWH, R, Python

• Supported customers in implementing an architecture design for extracting and preparing data
• Planned the design and implementation of the BI and DWH platform
• Ensured the scalability and performance of the data platform

• Advising companies on building, migrating and optimising BI and reporting landscapes (Power BI, SQL, Python, ETL)

• 5 client engagements in real estate and finance since 05/2025: taking over and stabilising existing reporting, automating recurring standard and management reports, building cash-flow models

• Proposal and feasibility assessments for BI and reporting projects

• Using AI-assisted development (Claude Code) to accelerate automation, tooling and web/app development

• Custom ERP system

• Problem: A client's core processes ran on scattered, siloed Excel files with no central data storage – error-prone, hard to scale and impossible to analyse end-to-end.

• Approach: Captured the business processes and requirements, modelled the data and developed iteratively together with the business team.

• Implementation: Built a tailored, web-based ERP system with a central database, role-based modules and automated reporting – delivered using AI-assisted development in Claude Code.

• Timesheet app

• Starting point: Time tracking based on an overgrown, macro-heavy Excel template – maintenance-intensive, single-user and error-prone.

• Implementation: Migrated all functionality and VBA macros into a standalone web app with central data storage, multi-user support and automated reporting.

• Cash-flow modelling

• Starting point: The existing cash-flow model covered standing investments only; project developments were missing from steering.

• Implementation: Built and extended the CF model to include project-development cash flows.

• Optimisation: Reviewed and optimised existing CF models and expanded the KPI outputs for reporting and steering.

UEF 3.0 · Semantic Government Overlay (SGO) · Autonomous Systems (UAS / dual use)

• Designed: Semantic Government Overlay (SGO) – AI-guided administration without replacing existing specialist procedures. Read-only semantic layer over registers and specialist processes based on the Federal Information Management (FIM). Decision authority remains with the case worker (architecture principle).
• Developed: Reference architecture with source-backed, derived statements (Executable Ontologies OWL/RDF/SHACL). Technically guaranteed purpose limitation and no-write-path principle in specialist data – auditable, without a central data pool.
• Anchored: Regulation as a design principle: EU AI Act (high-risk obligations for public-sector AI, fundamental rights impact assessment under Art. 27), GDPR, NIS2, and administrative automation limits (§ 35a VwVfG, § 31a SGB X) as technical control points in the architecture.
• Created: Methodical tool for pilot organizations: data pipeline assessment (phase 0), compliance blueprint, and management summary as a decision-ready package for public administration.
• Specified: UEF 3.0 as a successor architecture to TOGAF – decision paper, canonical ontology, six-layer architecture, read/actuate boundary, federation registry, terminology concordance, and release delta as a closed specification status.
• Architected: AI-native mission OS for autonomous UAS and ground robotics as a tactical layer on top of a separately approved autopilot. Run-time assurance according to ASTM F3269-21 (Simplex pattern): the verified safety controller keeps authority, the AI function provides suggestions.
• Designed: Three-tier architecture – Tier 0 autopilot with 650 Hz flight control on RTOS, Tier 1 AI OS with semantic world model and multi-agent cluster, Tier 2 swarm and ground mesh. Zenoh as the primary fabric, MAVLink as the only authenticated command path (single writer). Result: graceful degradation – loss of the mission, not of the aircraft.
• Secured: Two-gate chain on the read/actuate boundary – governance gate (can-question: AI Act risk class per actuation, enforced human oversight under Art. 14, immutable log) before the RTA safety monitor (is-it-correct question: flight envelope, geofence, energy reserve) with revert to the baseline controller.
• Anchored: Dual-use architecture with common core and build-time fork instead of runtime switch. Three separate legal levels: civil variant – UAS under the EASA Basic Regulation (EU) 2018/1139 with the limited applicability under Art. 2(2) of the AI Act, ground robotics under the Machinery Regulation 2023/1230 with the full high-risk obligation chain, Cyber Resilience Act for both; unarmed carrier variant as defense material under AWG/AWV and Dual-Use Regulation 2021/821 (BAFA approval); armed variant under KrWaffKontrG. Each variant lives under exactly one dominant legal regime. Evidence base: AI BOM, SBOM, and complete data lineage.
• Analyzed: System analysis and realignment of grown engineering system landscapes. Approach concept for consolidation without migration – semantic layer over the existing sources instead of data transfer. Result: decision-ready implementation concept including an evaluation model for the target architecture.

• Responsible for the architecture and development of an AI-native SaaS platform for industrial product portfolio management.
• Designed the modern data platform architecture on Azure for scalable analytics and enterprise data integration.
• Built enterprise data ingestion and transformation pipelines across complex industrial system landscapes.
• Developed graph-based representations of product structures and dependencies for analytical reasoning.
• Designed and implemented an agentic AI framework for AI-supported decision workflows.
• Built scalable analytical microservices and integrated reporting through modern BI technologies.
• Coordinated backend, AI, and frontend development across the MYNR platform stack.

• Built and maintained Power BI dashboards (DAX, Power Query, data modeling) tracking KPIs across 15+ global manufacturing sites — primary reporting tool for EU leadership decision-making.
• Developed a multi-screen Power Apps application (configurator-style tool) with SharePoint-based workflow integration for the sales team — designed jointly with business stakeholders and IT.
• Built and maintained automated Power Automate workflows connecting to SQL databases; independently identified and deployed an LLM-driven automation use case that eliminated 90% of manual reporting effort — self-pitched to leadership and taken end-to-end into production.
• Built a Python-based data pipeline (SQL) extracting, modeling, and validating data across 10+ EU plants — establishing reliable data models and KPIs for cross-site reporting.

Tasks: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.

Activities:

• Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
• Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
• Developed automated data pipelines with Python, dbt, and GCP services for different data sources
• Built monitoring and alerting systems for real-time platform monitoring
• Implemented data versioning and quality checks at every layer
• Designed automated test and deployment pipelines in GitLab and Bitbucket

Achievements:

• 2× production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
• Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, configuration errors to 0
• Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
• Migrated 7 database tables with 0 downstream issues
• Removed 100% exposed credentials, eliminated external vendor dependency
• Delivered integration of 3 teams in 1 sprint

• Partner with commercial, sales, and medical affairs teams to translate business questions into structured analyses and interactive Power BI dashboards, enabling data-driven decisions in a regulated pharma environment.
• Design and maintain Power BI dashboards that integrate data from Veeva CRM, SharePoint and Databricks, providing real-time visibility into sales trends, territory performance, and commercial KPIs across multiple markets.
• Query and join multiple tables in Databricks using SQL to build clean, analysis-ready datasets, applying transformations such as filtering, aggregation, and window functions to prepare data for reporting.
• Implement Power Automate flows to automate data refresh processes and trigger alerts for KPI thresholds, improving the timeliness and reliability of commercial analytics reporting.

• Data Engineering

• Business Analyst

• Consulting

• 2025 – present

Data Engineer & Architect

• Azure Data Factory
• Azure Databricks (PySpark)
• PowerBI Service

Lead developer for the further development of the Modern Data Warehouse, as well as Power BI reports and data models based on the ERP systems Amparex and EyeOffice. Focus on topics for the marketing department with data connection via API to Amplitude and Klaviyo with Python

• 2024 – present

Data Engineer & Architect

• SQL Server (T-SQL)
• SSIS (ETL)
• SSAS Tabular
• PowerBI Report Server
• Atlassian Jira & migration to Azure DevOps with GIT (KANBAN)

Development of Power BI reports and data models based on Infor LN ERP data after migration from Baan, including the underlying data structures with Microsoft SQL Server for further development of the Data Warehouse

Consulting for controlling on the specification of business requirements and development of reporting solutions

• 2025 – 06.2025

Data Engineer & Architect

• PowerBI Dataflows
• PowerBI Service

Cloud migration of the Data Warehouse, as well as Power BI reports and data models from Microsoft Dynamics BC 2021 to Microsoft Dynamics BC Cloud, including migration of the underlying data structures from Microsoft SQL Server to Power BI Dataflows

• 2022 – 06.2024 (ongoing support for data engineers)

Data Engineer & Architect

• SQL Server (T-SQL)
• SSIS (ETL) & BIML
• SSAS Tabular
• Azure DevOps with GIT (SCRUM)

Development of analytical models based on Microsoft Dynamics BC 2021 in combination with data from the company's own MariaDB database, including the setup of the underlying data structures with Microsoft SQL Server to build a Data Warehouse. Merging of the ERP systems BC 2021 and DATEV in the Data Warehouse

Consulting for business users on the specification of business requirements and coaching of developers for the development of reporting solutions

• 2022 – 08.2023

Business Analyst & Interim Manager

• PowerBI Service
• Azure Databricks (PySpark)
• Atlassian Jira (SCRUM)

Project management for reporting solution requirements, as well as development of Power BI reports and data models with data from Azure Databricks based on SAP R/3, Google Analytics, webshop and CRM with a business focus on customer, sales and marketing

• 2022 – 05.2022

Power BI Specialist

• PowerBI Service
• Azure DevOps with GIT (SCRUM)

Development of Power BI reports and data models based on data from Exasol with a business focus on assortment management in purchasing

• 2021 – present (ongoing support for existing customers)

Power BI Specialist & Data Engineer

• SQL Server (T-SQL)
• SSIS (ETL) & BIML
• PowerBI Service
• Azure DevOps with GIT (KANBAN)
• Azure Functions (Python)

Development of Power BI reports and data models based on abas ERP data, including the setup of the underlying data structures with Microsoft SQL Server to build a Data Warehouse.

Synchronization of customer data for Pipedrive CRM via Azure Functions with Python.

Consulting for controlling and management on agile project management, the specification of business requirements and development of reporting solutions

• 2021 – present (ongoing support for existing customers)

Data Engineer & Architect

• SQL Server (T-SQL)
• SSIS (ETL) & BIML
• SSAS Tabular
• Azure DevOps with GIT (SCRUM)

Development of analytical models based on Microsoft Dynamics 365 FO, CS & CRM in combination with Microsoft Dynamics AX 2012 R3, including the setup of the underlying data structures with Microsoft SQL Server to build a Data Warehouse

Concept for the migration of the SQL Server-based Data Warehouse to Microsoft Fabric with planned future implementation and temporary use of a hybrid data architecture for the introduction of Power BI Service during the migration phase

Consulting for business users on the specification of business requirements and development of reporting solutions

• Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
• Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
• Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
• Use of Scrum and Kanban methods.
• Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
• Development of new plugins and add-ons needed on current infrastructure.
• Database support.
• Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
• Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
• Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
• Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
• Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
• Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
• Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
• Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
• Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
• Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
• Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
• Automated system provisioning and deployment using CloudFormation templates.
• Configuration of IAM roles, policies and permissions to ensure secure access control.
• Patch management, backup automation and disaster recovery setup on AWS infrastructure.
• Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
• Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
• Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
• Configuration of AWS CloudWatch to monitor application performance and system events.
• Planning and execution of migration of on-premises applications to AWS cloud platforms.
• Deployment of containerized applications using Docker and Kubernetes in AWS environments.
• Deployment of internal software packages between availability zones using AWS CodeDeploy.
• Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.

• Lecturer for the GenAI Track at the Master School Institute of Technology
• Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation

• KPI dashboards for inventory and goods received quality control & testing ETL, dataset, dataflows and report development, complex DAX solutions

Data sources: Dataverse, RDB, Excel

Tools: ETL, data modeling, data flows, Power Query, M, Power BI, complex DAX

• Capacity: 25%

• Establishing the Microsoft Fabric platform with the company as the central data warehouse and reporting system.
• The company migrated several operational systems including D365 to new version. The new data warehouse is based on a Fabric/Power BI architecture with Azure cloud Entra Authentication.

Tasks Performed:

• Full administrative responsibility for the Fabric platform F64 and F32 capacities, as well as two F8 capacities for development and prototyping.
• Integration of Fabric with Microsoft Entra.
• Data modeling for the data warehouse bronze, silver and gold layers using data modeling tools.
• Star Schema as well as entity relationship modeling.
• Data modeling for data marts and for dimensional modeling and Power BI (semantic model).
• Design of data models for Microsoft SSAS multidimensional and tabular OLAP cubes using DAX and MDX.
• Definition of design patterns for the ETL team for loading OLAP cubes (Tabular and MD), star schemas and data vault structures.
• Planning and managing team of 4 ETL and reporting developers.
• Performance Tuning of Power BI reports, particularly the semantic layer, as well as the Fabric notebooks
• SQL Performance Tuning.
• DB Design for Azure SQL.
• Reporting directly to project senior management.

Label: Power BI, Fabric, Analysis Services (multidimensional and tabular), D365, SSIS, Tabular Editor, SQL Server and Azure SQL, TOAD Data Modeler, DBSchema, Visual Studio Code, DAX Studio, SSMS, Visual Studio, Jira and Confluence.

Enterprise & Cloud Security Architect supporting the modernization of the SDK application landscape as part of the KVNeo transformation program. Responsible for enterprise architecture, cloud governance, security architecture, and the definition of technical standards for strategic business applications.

Key responsibilities include architecture governance, target architecture development, cloud and integration architecture, security-by-design, and the translation of regulatory requirements into sustainable technical solutions across multiple business domains.

Responsibilities and achievements

• Designed and reviewed target architectures for strategic insurance applications and enterprise services.
• Developed architecture documentation based on Arc42 and Architecture Decision Records (ADRs).
• Defined governance models, architecture principles, and technical guidelines for cross-domain initiatives.
• Supported the modernization of archive, document management, and output management platforms.
• Designed integration architectures using REST APIs and event-driven communication patterns.
• Led architecture discussions with enterprise architects, development teams, product owners, and business stakeholders.
• Translated regulatory requirements such as DORA and ISO/IEC 27001 into practical architecture decisions.
• Designed security concepts covering Identity & Access Management, authorization, authentication, auditability, and logging.
• Supported SIEM integration, security monitoring, and enterprise logging concepts.
• Evaluated technical risks, technical debt, and architecture improvements while providing decision papers for architecture boards.
• Established architecture governance processes and contributed to enterprise-wide transformation initiatives.
• Supported cloud governance activities and the definition of secure cloud architecture standards.
• Facilitated architecture workshops and coordinated cross-functional stakeholders across business and IT.

Technologies & Methods Microsoft Azure • Arc42 • Architecture Decision Records (ADR) • REST APIs • Event-Driven Architecture • Microsoft Entra ID • Active Directory • IAM • SIEM • Cloud Governance • Enterprise Architecture • Security Architecture • Azure API Management • Jira • Confluence • Draw.io • DORA • ISO/IEC 27001 • Agile • Scrum