Semantic Layer Experts in Germany

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.

• Thesis Title: Designing a Semantic Layer for Enterprise Analytics and AI Integration.
• Designed a semantic layer architecture for enterprise analytics that improved dataset discovery, relationship mapping, and governed access to reporting data.
• Built schema discovery and SQL generation workflows across 400+ data products, translating complex data structures into consumable analytics assets.
• Implemented role-based access control, Row-Level Security, and query validation to ensure data quality, governed access, and reliable use of enterprise reporting datasets.

• 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.

• Analysis of the data model for a DWH project with the Federal Network Agency
• Analysis of the data model and performance in Oracle
• Performance optimization in Oracle
• Adaptation of the schema model and expansion/optimization of dossiers
• Modeling of an internal finance DWH and development of reports for internal controlling
• Modeling of the stage, core, business, and data mart layers
• Definition of requirements for the ETL colleagues
• Replacement of the existing reporting tool Cubeware with PowerBI
• Building the semantic layer in PowerBI and development of reports

• Design and provide best practices on data modeling for dbt, including changing dimensions, late arriving data handling, and testing
• Design the ingestion flow from other systems into S3 and Redshift
• Design and implement new partitions for Dagster and incremental loading with dbt
• Map business requirements to technical architectures
• Instruct junior team members

• Supporting national and international companies in building data-driven processes, methods, systems, and applications (Data-Driven Company) in data management and agile requirements engineering.

• Identifying essential use cases and (non-functional) requirements to build an architecture on the one hand.

• Optimizing clients' internal processes and developing training plans for new technologies and methods.

• Combining technical know-how (for example, data analysis in cloud data analytics environments through semantic layers) with key soft skills such as agility, teamwork, creativity, and analytical competence.

• Emphasis in data management on efficient use of systems as well as the simple application of technologies for data engineering, data cataloging, virtualization, exploration, and visualization.

• Operationalizing both infrastructure as well as data and mathematical analytical models (DevOps, DataOps, MLOps).

• Acting as a link between architecture, business departments, development, and operations, taking into account essential core areas such as data governance, data security, and data quality.