Semantic Layer Experts in Germany
with fast, precise AI matching from over 15,000 CVsHire experts who define governed metrics, connect analytical models to BI tools and implement dbt Semantic Layer or LookML workflows. FRATCH matches you quickly with vetted, available freelancers who fit your technical and project requirements.
Meet FRATCH Experts in Germany, who have recently used Semantic Layer
Dilip S.
Last position:
Freelance Consultant at Self
Available for interim and contract mandates
Chisom N.
Last position:
Founder & Analytics Engineer
- Client — Podimo ApS (podcast & audiobook streaming): build the finance reporting layer on BigQuery + dbt + Airflow, including the core revenue-transaction fact tables used across finance reporting.
- API automation: design and build a BigQuery → Airflow → Microsoft Dynamics 365 Business Central REST-API pipeline to automate sales-invoice posting, with idempotency and master-data sync between systems.
- Delivery: sole engineer on the engagement — requirements, modelling, orchestration and stakeholder communication with the client finance team, end to end.
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.
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.
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.
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.
Mojtaba P.
Last position:
Data and AI consultant at Self employed
Helping companies adapt AI into their BI stack, agentic BI, knowledge graphs, internal company RAG system, semantic layers, BI harness, KPI alerts, data warehousing, etl, elt, data modelling, reporting layer with AI touch
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-centric processes, methods, systems and applications (Data-Driven Company) in the areas of Data Management and agile Requirements Engineering.
Identifying essential use cases and (non-functional) requirements to build an architecture from them.
Optimizing the client's internal processes and developing training plans for new technologies and methods.
Combining technological know-how (for example, for data analysis in Cloud Data Analytics environments through semantic layers) with important soft skills such as agility, team skills, creativity and analytical competence.
Focusing in the area of Data Management on the efficient use of systems and the simple use of technologies for Data Engineering, data cataloging, virtualization, exploration and visualization.
Operationalizing infrastructure as well as data and mathematical and analytical models (DevOps, DataOps, MLOps).
Acting as the link between architecture, business departments, development and operations while taking essential core areas such as Data Governance, Data Security and Data Quality into account.
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
5 years
Positions per freelancer
11
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Insurance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
17%
Certifications per freelancer
4
Most common languages
German, English, Hindi
Speak two or more languages
80%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging 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 29 Oct 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 (80%)
- Banking and Finance (60%)
- Insurance (50%)
- Manufacturing (50%)
- Automotive (40%)
- Professional Services (40%)
- Retail (40%)
- Transportation (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What a Semantic Layer does
A semantic layer creates a consistent business meaning for data. It defines metrics, dimensions, relationships and calculation rules once, then makes them available across dashboards, reports, analytics applications and machine learning workflows. Teams can ask for revenue, retention or margin using shared definitions instead of rebuilding logic in every query.
Core implementation work
Specialists model business entities and metrics on top of warehouses such as Snowflake, BigQuery, Redshift or Databricks. They manage joins, filters, access rules, time logic and reusable measures while keeping query performance under control. The work can include dbt Semantic Layer, LookML, Cube, AtScale or a custom metrics layer built around existing data infrastructure.
- Define trusted metrics and dimensions
- Map source tables to business concepts
- Connect models with BI and analytics tools
- Test calculations, lineage and permissions
Tools and adjacent skills
Effective delivery depends on more than metric definitions. Professionals often work with SQL, dbt, data warehouses, transformation pipelines, governance practices and BI tools such as Looker, Tableau, Power BI or Superset. Knowledge of APIs, version control, testing and data contracts helps teams publish the same logic safely to different consumers.
When companies bring in specialists
Companies usually seek freelance expertise when reports disagree, a warehouse is expanding, or self-service analytics needs stronger controls. A specialist can establish a governed model during a migration, replace duplicated dashboard logic, or prepare trusted metrics for embedded analytics. In Germany, collaboration may involve remote teams across regions as well as on-site workshops with business and data stakeholders.
- Reconcile conflicting KPI definitions
- Introduce a metrics layer during a warehouse project
- Improve trust in self-service reporting
- Prepare reusable data products for multiple teams
What strong professionals deliver
Strong professionals begin with the business vocabulary and trace every metric back to reliable source data. They make definitions understandable to analysts and stakeholders, while keeping models modular, documented and efficient. They also establish review, testing and change processes so a semantic model remains dependable as data sources and business rules evolve.
How to assess project fit
Look for experience with the warehouse, transformation stack and BI environment already in use. Ask for examples of metric governance, handling of many-to-many relationships, row-level security, performance tuning and model versioning. Clear documentation, careful validation and the ability to explain trade-offs are as important as tool-specific knowledge. For cross-border work in Germany, agree early on working language, workshop format and access requirements.
Frequently asked questions
What clients ask us most about Semantic Layer — answered in short.
A Semantic Layer translates technical data structures into governed business concepts such as revenue, orders, customers and retention. It lets BI tools, applications and analysts use shared definitions without copying calculation logic across reports.
A metrics layer centralizes definitions and relationships instead of leaving every dashboard to implement its own SQL. This improves consistency and change control, while direct SQL can remain useful for exploration or highly specialized analysis.
The dbt Semantic Layer is a product and workflow for defining metrics close to dbt-managed transformation models and serving them to supported consumers. A general semantic model can be implemented with other tools, such as LookML, Cube, AtScale or an in-house service.
A Semantic Layer specialist should understand SQL, data warehouse design, dbt or another transformation approach, and the BI tools that consume the model. Data governance, testing, lineage, APIs and access control are also valuable when metrics serve many teams.
A Semantic Layer project needs enough experience to understand both business definitions and warehouse behavior, not just the configuration of a tool. The right level depends on the number of source systems, metric complexity, governance needs and the state of the existing data model.
Semantic Layer work is often suitable for remote collaboration because models, documentation and reviews are handled digitally. German companies may still prefer on-site discovery sessions, and teams should clarify language expectations, stakeholder availability and secure data access before work starts.
A strong Semantic Layer has definitions that stakeholders understand and can trace to trusted source data. Review test coverage, documentation, lineage, permission handling, query performance and how safely the model supports changes without breaking downstream reports.
A Semantic Layer can provide consistent metrics to embedded dashboards, customer-facing analytics and internal data products through BI integrations or APIs. The implementation must account for tenant isolation, authorization, response time and the different ways application users consume data.
The average hourly rate of freelancers in Germany who have used Semantic Layer in their recent projects is 114 €, which corresponds to a daily rate of about 915 € 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 17% 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 5 years.
The most common languages among freelancers in Germany who have used Semantic Layer in their recent projects are German (100%), English (80%), and Hindi (20%).
The most common industries among freelancers in Germany who have used Semantic Layer in their recent projects are Information Technology (80%), Banking and Finance (60%), and Insurance (50%).
The most common business areas among freelancers in Germany who have used Semantic Layer in their recent projects are Information Technology (100%), Business Intelligence (90%), and Product Development (80%).
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
