Data Vault Experts in Germany
, matched in minutes with vetted, available specialistsHire experts who design Data Vault 2.0 models, automate ELT pipelines and connect cloud data platforms with reporting layers. FRATCH uses precise AI matching to help you find vetted, available freelancers quickly.
Meet FRATCH Experts in Germany, who have recently used Data Vault
Alexander Z.
Last position:
Senior Data Solutions Engineer at VMware Inc.
- Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
- Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
- Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
- Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
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.
Jorge M.
Last position:
Technical Lead / Fractional CTO at Würth GmbH
I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.
Main Tasks:
- Sprint planning and feature preparation
- Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
- Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
- Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
- Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
- Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
- Manage production releases and execute live data migrations for enterprise customers
- Define engineering standards and architecture patterns for the team
Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL
Emanuel F.
Last position:
Interim Architect & Data Taskforce at Freelancer / Project Assignments
- Data Engineering: Design and implementation of scalable data pipelines
- Legacy migrations to Microsoft Fabric (Lakehouse, Dataflows Gen2, Pipelines)
- BO Universe migrations to MS Fabric / Semantic Models / Power BI
- Taskforce for data-driven transformation projects involving Azure Fabric / Oracle / MSSQL
Serge K.
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- 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
Olaf T.
Last position:
BI Architect in a Kanban Team at Gulp / Randstad for HHLA
- Creation of automated tests.
- Extension of the functionality of the enterprise BI data warehouse.
- Design and development in SQL and PL/SQL.
- Data modeling as well as performance measurement and tuning.
- Creation of and contribution to user stories and concepts.
- Technical environment: Windows, Linux.
- Database: Oracle 19c incl. DWH, CDB, PDB.
- Tools: Confluence, CVS, Eclipse, Git, GitLab, Jira, N4, PL/SQL, SQL Developer, utPLSQL.
Thore F.
Last position:
Data & AI Consultant at ContiTech GmbH
- Consultancy Databricks Lakehouse Platform Architecture
- Migration of existing manufacturing data services to Databricks. Existing services are running on various platforms & tools and are unified on target platform
- Implementation of new Data & AI use cases on Databricks platform (e.g. connecting new systems, building AI Agent prototypes, …)
Tech Stack: Databricks, Azure, PySpark, Python
Matthias W.
Last position:
Freelance Consultant Business Intelligence (Self-employed) at mw-consult.it
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
Michael T.
Last position:
ETL Developer at Insurance service provider
DWH for customer and financial data
- Extension of the DWH with new data sources
- Report development
- Data quality management
Methodology: Scrum
Tools: Atlassian Confluence & Jira
Databases: Microsoft SQL Server
Programming languages: SQL, T-SQL
ETL: Microsoft SQL Server Integration Services (SSIS)
Frontend platform: PowerBI, Microsoft Reporting Services
Enrico G.
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- 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
Johannes W.
Last position:
Senior Data Engineer at Soorce GmbH
- Analysis of business requirements
- Integration of different data sources such as ERP systems, production systems, and external data sources
- Implementation of load processes and processing logic with MSSQL
- Data modeling and optimization of data models
- Setting up data quality management incl. data profiling with dynamic programming
- Support in designing and establishing data governance, especially in the areas of data quality management and data protection
- Support in developing BI solutions with Tableau to help decision-making processes
Olga M.
Last position:
Telefónica Deutschland Holding AG
- Implemented the BI solution and replaced the old BI landscape
- Led the “Sales Bonus Plan” subproject: expanded the existing solution and migrated to Azure (Databricks)
- Led the “Cognos Migration” subproject: expanded existing data warehouses based on MS technologies (source systems: Oracle), analyzed business requirements and designed the technical solution
- Expanded the relational DWH database
- Developed ETL processes using SSIS
- Developed the multidimensional database (OLAP Cubes/SSAS)
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.
Umut G.
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Moez S.
Last position:
Data Engineer at Loschelder Rechtsanwälte Partnerschaftsgesellschaft mbB
- Designed a future-proof client database for marketing purposes
- Analyzed requirements, designed, and modeled an entity-relationship model
- Consolidated and optimized a client file from various data sources for targeted marketing campaigns
- Worked closely with marketing and IT in an agile environment to iteratively develop the solution
- Technologies and methods: MS Office (mainly Excel), MS Dynamics CRM, MS SharePoint
Discover over 15,000 top freelancers
Statistics of experts using Data Vault
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
1.9 years
Positions per freelancer
18
Top business areas
Business Intelligence, Information Technology, Project Management
Top industries
Information Technology, Insurance, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
76%
Master's degree or higher
52%
Doctorate
12%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
97%
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 Data Vault
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.
Data Vault 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 (91%)
- Insurance (59%)
- Banking and Finance (56%)
- Professional Services (53%)
- Retail (50%)
- Automotive (47%)
- Telecommunication (47%)
- Manufacturing (44%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Vault is
Data Vault is a data warehouse modeling method built for change, auditability and long-term integration. It separates business keys in hubs, relationships in links and descriptive context in satellites. Data Vault 2.0 extends the approach with stronger automation, agile delivery and broader architecture guidance.
What it builds
Companies use Data Vault to create resilient analytical foundations from ERP, CRM, operational and external sources. It supports enterprise reporting, self-service analytics, regulatory traceability and data products without forcing every source into one rigid model.
- Integrate changing source systems
- Preserve historical business context
- Support audit-ready data lineage
- Feed dimensional marts and analytics layers
Ecosystem and tooling
Data Vault work commonly spans cloud warehouses, lakehouses and ELT orchestration. Strong specialists understand SQL, metadata-driven automation, dbt, Snowflake, Azure Synapse, Databricks or Google BigQuery where relevant. They also connect ingestion, testing, documentation and semantic modeling around the vault.
When expertise matters
Bring in freelance expertise when a warehouse is expanding across domains, source structures change frequently or delivery teams need a consistent modeling standard. It is also valuable during platform migrations, Data Vault 2.0 adoption and efforts to replace fragile hand-built transformations.
- Define hubs, links and satellites from business concepts
- Review loading patterns and historization rules
- Establish naming, metadata and testing standards
- Improve pipeline reliability and delivery flow
What strong professionals do
The best professionals connect business meaning with physical implementation. They distinguish true business keys from technical identifiers, manage effectivity and record-source fields carefully, and document assumptions. They make trade-offs clear instead of applying Data Vault patterns mechanically.
Germany-based collaboration
Projects in Germany often involve complex enterprise landscapes, distributed stakeholders and strict expectations for documentation and traceability. Remote collaboration works well when modeling decisions, source definitions and acceptance criteria are recorded clearly. On-site workshops can help align domain specialists, platform teams and reporting stakeholders during critical design phases.
Frequently asked questions
Not sure where to start with Data Vault? These answers cover the essentials.
Data Vault is used to build an adaptable, historical data warehouse layer from multiple changing sources. It helps companies preserve raw context, trace transformations and add new domains without redesigning the entire warehouse.
Data Vault separates integration and history from downstream business presentation, while dimensional modeling usually organizes data directly into facts and dimensions for analysis. Many modern programs use both: the vault for a governed foundation and dimensional marts for reporting.
Data Vault 2.0 expands the modeling method with guidance for agile delivery, automation, performance, quality and broader data architecture. It is not simply a naming change; it places more emphasis on repeatable processes and an integrated delivery environment.
A strong Data Vault specialist should understand SQL, dimensional modeling, ELT orchestration, data quality, metadata and cloud warehouse design. Experience with tools such as dbt, Snowflake, Databricks or Azure Synapse can be valuable when they match the project landscape.
The right level depends on scope, source complexity and whether the organization already has modeling standards. For a new enterprise foundation, look for a professional who can lead architecture decisions and stakeholder workshops; a smaller extension may need focused modeling and pipeline expertise.
Yes. Data Vault projects can be delivered remotely when source definitions, modeling decisions and review routines are documented. For teams in Germany, German-language workshops may help with business alignment, while technical delivery can often be coordinated in English.
Review whether business keys, hubs, links and satellites are justified rather than created by habit. Good work includes clear lineage, repeatable loading logic, well-defined historization, automated tests and documentation that another professional can use.
Data Vault may add unnecessary structure for a small, stable source set with simple reporting needs and little history to preserve. It can also disappoint when a team adopts the pattern without investing in business-key definitions, automation, testing and downstream modeling.
The average hourly rate of freelancers in Germany who have used Data Vault in their recent projects is 107 €, which corresponds to a daily rate of about 855 € based on an 8-hour working day.
Of the freelancers in Germany who have used Data Vault in their recent projects, 76% hold at least a Bachelor's degree, 52% hold at least a Master's degree, and 12% hold a doctorate.
On average, freelancers in Germany who have used Data Vault in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Germany who have used Data Vault in their recent projects are German (100%), English (97%), and French (26%).
The most common industries among freelancers in Germany who have used Data Vault in their recent projects are Information Technology (91%), Insurance (59%), and Banking and Finance (56%).
The most common business areas among freelancers in Germany who have used Data Vault in their recent projects are Business Intelligence (100%), Information Technology (100%), and Project Management (76%).
Main locations of FRATCH Experts, who have recently used Data Vault
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 
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FRATCH CEO
