Data Vault Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Data Vault
Umut Gülac
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.
Alexander Zhirov
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 Kmiecik
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 Machado
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
Moez Seyedan
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
Guido Klein
Last position:
NOBILIS Group GmbH
- Support during SSRS implementation, including training employees on SSRS
Olaf Taesch
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 Fahrtmann
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
Serge Kalinin
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
Matthias Wähler
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 Ternes
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 Goerlitz
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 Wagner
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
Mario Techera
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.
Olga Methner
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)
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
78%
Master's degree or higher
52%
Doctorate
13%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
97%
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Data vault basics
Data Vault is a data warehouse modeling approach for building flexible, traceable analytical platforms. It separates stable business keys, descriptive details, and relationships so teams can absorb change without rewriting the whole model. It is often chosen for enterprise reporting, historical tracking, and regulated data environments.
Where it fits
Companies bring in Data Vault specialists when source systems change often or when many domains must be integrated into one warehouse. It is used for:
- enterprise data warehousing
- audit-friendly history and lineage
- multi-source integration
- staging for BI and semantic layers
- legacy warehouse modernization
Core methods
A strong specialist knows hubs, links, and satellites, plus how to model business keys and changing attributes. Many projects also use Data Vault 2.0 patterns, hash keys, and rules for loading at scale. The work must stay consistent across domains so history, traceability, and performance all hold together.
Tooling around it
Data Vault rarely stands alone. Specialists often work with SQL, ETL or ELT tooling, orchestration, warehouse engines, and data quality checks.
- SQL modeling and load logic
- dbt or similar transformation layers
- orchestration and scheduling
- warehouse platforms and cloud storage
- testing, monitoring, and reconciliation
When freelancers help
Freelance experts are useful when a team needs a clean design review, a fast start on a new warehouse, or rescue for a model that has become hard to maintain. In Germany, this often comes up in enterprise environments with mixed legacy systems, strict reporting needs, and teams working across locations. Remote collaboration is common, but on-site workshops help when source systems and business rules are still unclear.
What strong experts deliver
Good Data Vault professionals do more than draw tables. They translate business keys, source rules, and change handling into a model that is understandable and loadable.
- clear naming and consistent layering
- reproducible load patterns
- documented lineage and history
- pragmatic performance choices
- handover that lets internal teams extend the model
Frequently asked questions
Not sure where to start with Data Vault? These answers cover the essentials.
Data Vault is used to build analytical data warehouses that can absorb change without losing history. It is a good fit when many source systems feed one platform and the business needs traceability, lineage, and long-term consistency.
Data Vault focuses on integration, history, and auditability, while a star schema focuses on easy consumption for reporting. Many teams use Data Vault as the warehouse backbone and then publish star schemas or marts on top of it.
Bring in a Data Vault specialist when the model must be designed from scratch, when an existing warehouse is hard to change, or when load patterns need to be stabilized. Freelancers are also useful for reviews, workshops, and moving a team toward Data Vault 2.0 practices.
A strong Data Vault expert usually knows SQL very well and understands ETL or ELT design, orchestration, data quality, and warehouse performance. Domain modeling and clear documentation matter just as much as tool knowledge.
No. Data Vault is the overall modeling approach, while Data Vault 2.0 refers to a newer set of practices and conventions around hashing, automation, scalability, and implementation discipline. Many job briefs and project discussions use both terms, so it helps if the expert knows the differences.
Yes, Data Vault work is often done remotely because modeling, SQL design, and review sessions can be handled online. In Germany, short on-site sessions can still help at the start of a project, especially for domain workshops and source-system analysis.
Data Vault projects often rely on SQL, dbt, warehouse platforms, orchestration tools, and testing frameworks. The exact stack depends on whether the team builds on cloud warehouses, legacy databases, or a mixed environment.
Look for a Data Vault professional who can explain hubs, links, and satellites in plain language and show how they would load and test them. Strong candidates also ask about source keys, history rules, reconciliation, and how the model will be consumed by downstream analytics.
The average hourly rate of freelancers in Germany who have used Data Vault in their recent projects is 108 €, which corresponds to a daily rate of about 868 € based on an 8-hour working day.
Of the freelancers in Germany who have used Data Vault in their recent projects, 78% hold at least a Bachelor's degree, 52% hold at least a Master's degree, and 13% 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 (28%).
The most common industries among freelancers in Germany who have used Data Vault in their recent projects are Information Technology (91%), Insurance (63%), 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 (75%).
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 
Frankfurt Request a free demo
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Philipp Thomaschewski
FRATCH CEO
