Semantic Layer Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Semantic Layer
Justina K.
Last position:
Freelance Consultant for Change & Data Transformation at Freelance Fast Data Consulting
Project, Strategic Consulting – building the Data Strategy and Data Governance Policy for the German branch, client (private bank Julius Bär, headquarters Zurich), March 2026 – present
- Design and negotiation of the data strategy with key stakeholders, including obtaining board sign-off (strategic consulting) – in this context, regulatory advice on data regulations in the EU and specifically for Germany. The data strategy includes: Data Lifecycle Management: data capture, data storage, data usage, data retention policy, data quality incident management
- Definition of milestones and technical feasibility for implementing TOM for the data strategy, data quality checks, metrics, and a metadata inventory to ensure the bank’s compliance with DORA, BCBS239, and MaRisk requirements.
Core project data change, client: (ING Bank, Frankfurt am Main), March – December 2025
- Concept development and solution design for new end-to-end processes including technical interfaces
- Definition of synchronization logic and data flows between legacy and target systems (decommissioning of legacy systems)
- Analysis and validation of data models
- Stakeholder communication with product owners, feature engineers, UX designers, and operational teams for decision-making
- Analytics and impact assessments, e.g. to assess downstream effects and regulatory requirements
- Documentation and comments on technical and business requirements to support implementation in agile squads
Project digitalization of a user group, client: (ING Bank, Frankfurt am Main), as Interim Product Owner, Jan 2025 – present
- Co-shaping key decisions on data architecture and process logic in the context of historized data and user login functionality
- Development of business solution concepts for migration to the target system, including system integration and data flows
- Support with analytics and impact analyses, especially regarding the ability to provide information to law enforcement authorities
- Active coordination with stakeholders from different squads to support decision-making and ensure regulatory requirements are met
- Creation of test scenarios for operational teams and backend systems in the area of API management using Postman and Bruno.
Andreas W.
Last position:
Enterprise Architect at Own development / IP of CAMCO Engineering UG
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.
Syed Muhammad Aun Raza Z.
Last position:
Master Thesis - Semantic Layer at Linde GmbH, Linde Engineering - Commercial Department
- 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.
Mario T.
Last position:
Project Lead/Manager / Consultant at all-BI GmbH
- 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.
Matthias L.
Last position:
IT service provider
- 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
Jorge M.
Last position:
Data Architect at Deutsche Bahn
- 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
Oliver R.
Last position:
Trainer and Solution Architect for Data Management, Data Mesh, Data Fabric, Observability, Big Data Technologies, Advanced
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.
Discover over 15,000 top freelancers
Statistics of experts using Semantic Layer
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
6.1 years
Positions per freelancer
12
Top business areas
Business Intelligence, Information Technology, Operations
Top industries
Information Technology, Insurance, Manufacturing
Certification focus areas
Business Intelligence, Information Technology, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
33%
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
71%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using Semantic Layer
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Semantic Layer experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (71%)
- Insurance (57%)
- Manufacturing (57%)
- Automotive (43%)
- Banking and Finance (43%)
- Government and Administration (43%)
- Retail (43%)
- Chemical (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it does
A semantic layer turns raw warehouse tables into business-ready definitions. It gives teams one place for metrics, dimensions, and naming rules so reports, dashboards, and self-serve analysis stay consistent. It is often discussed as a metrics layer or semantic model, and in some stacks as the dbt Semantic Layer.
Where it fits
- BI dashboards and executive reporting
- Self-serve analytics for product, finance, and sales teams
- Shared metric definitions across tools
- Data products that need governed access to trusted fields
Strong specialists know how to keep calculations stable across tools like Looker, Tableau, Power BI, dbt, and warehouse-native stacks.
Key skills
A strong professional understands SQL, dimensional modeling, metric logic, and data warehouse design. They also know how to manage joins, filters, row-level rules, and naming so business users can trust what they see. Good work here is precise, not flashy.
When companies bring in help
Teams usually need freelance expertise when metrics have drifted, dashboards disagree, or a warehouse has grown too messy for ad hoc definitions. This often happens during BI migrations, analytics replatforming, or when a company in Germany needs cleaner collaboration between local analysts and remote data teams.
What good work looks like
- Clear metric definitions with documented business logic
- Consistent joins and reusable dimensions
- Access rules that match data governance needs
- Performance-aware queries and clean naming
- Documentation that analysts can actually use
The best specialists also think about change management. They help teams keep definitions aligned as products, funnels, and finance rules evolve.
Common project outcomes
A project may deliver a central semantic model, a governed metrics layer, or a migration from scattered spreadsheet logic into shared data definitions. Companies often want fewer manual fixes, faster analysis, and fewer arguments about the “right” number. That is the practical value of semantic work.
Frequently asked questions
What clients ask us most about Semantic Layer — answered in short.
A semantic layer translates raw data into business terms that analysts and decision-makers can use without rewriting logic in every dashboard. It is used to define metrics, dimensions, filters, and permissions in one place. That makes reporting more consistent across BI tools and teams.
A semantic layer is the broader idea: a business-facing model over technical data. A metrics layer is often a narrower part of that, focused on shared measures such as revenue, active users, or conversion rate. In practice, many teams use the terms together, especially when they work with dbt Semantic Layer or similar tooling.
A semantic layer specialist is useful when dashboard numbers do not match, metric logic lives in too many places, or a data warehouse has become hard to govern. Companies also bring in help during BI migrations or when they want to standardize definitions across teams. The right expert can untangle the logic without breaking existing reporting.
A semantic layer project often touches dbt, Looker, Tableau, Power BI, Snowflake, BigQuery, or Databricks. The exact stack depends on where the business logic should live and how the team serves analytics today. Strong specialists adapt the design to the tools already in place.
A semantic layer specialist should be comfortable with SQL, dimensional modeling, warehouse design, and data governance. Familiarity with BI semantics, API-driven data access, and documentation also helps. The best professionals can talk to analysts and technical teams with equal clarity.
A semantic layer project can be small if it only covers a few shared metrics, but it quickly gets complex when many teams rely on the same definitions. For that reason, companies usually want someone who has handled metric modeling, join logic, and tool integration before. Experience with messy reporting environments matters more than generic data work.
Yes, semantic layer work is often well suited to remote collaboration because most tasks happen in SQL, modeling tools, and documentation. For teams in Germany, the key is usually good communication in English or German and fast feedback with analysts and stakeholders. On-site sessions can still help at the start of a migration or design workshop.
A semantic layer project is strong when business users get the same answer in every tool and can understand where each metric comes from. Look for clear naming, documented logic, stable joins, and sensible governance rules. Good specialists also leave behind a model that other experts can maintain without guesswork.
The average hourly rate of freelancers in Germany who have used Semantic Layer in their recent projects is 121 €, which corresponds to a daily rate of about 970 € based on an 8-hour working day.
Of the freelancers in Germany who have used Semantic Layer in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Germany who have used Semantic Layer in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 6.1 years.
The most common languages among freelancers in Germany who have used Semantic Layer in their recent projects are German (100%), English (71%), and Spanish (14%).
The most common industries among freelancers in Germany who have used Semantic Layer in their recent projects are Information Technology (71%), Insurance (57%), and Manufacturing (57%).
The most common business areas among freelancers in Germany who have used Semantic Layer in their recent projects are Business Intelligence (100%), Information Technology (100%), and Operations (71%).
Main locations of FRATCH Experts, who have recently used Semantic Layer
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
