Data Vault Experts in Frankfurt
, matched in minutes from over 15,000 CVs with the power of AIHire experts who design resilient Data Vault models, automate ELT pipelines and connect cloud data platforms with reporting layers. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Frankfurt, who have recently used Data Vault
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.
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.
Ulm P.
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Petru K.
Last position:
Architect & Technical Team Lead & Senior Developer at Goetel GmbH
- Design, architecture & development/programming of ETL/ELT data pipelines, DWH, BI solution
- Technical project lead, POC – proof-of-concept creation
- Liaison between business units and technical teams
- Azure DevOps Boards & Jira
- Data modeling & data engineering – data warehouse & data mart
- Azure (Data Factory, Azure SQL, Azure DevOps CI/CD, Azure Data Lake V2, Business Central REST API, OData API, OAuth2 tokens)
- SharePoint lists & API for ADF, Firebird DB, Postgres DB, DB2
- Power BI (Power Query), DAX, Excel PBI add-on, GIS data
- Automated ETL process monitoring/logging, performance monitoring, error monitoring – capturing & resolution
- Index performance tuning & statistics monitoring, Transact-SQL
- Data security – MFA (multi-factor authentication) & OAuth2, MS Graph, Azure networks & firewalls, gateways, roles, user groups – with read/write permissions
- Sources – Vario Bill, Camunda, Radius, Geo Database, OTRS, PAST, MS Dynamics Business Central, Azure Blob Data Lake, SharePoint lists
Markus G.
Last position:
Data Solution Architect, Founder at GRITCON GmbH
- Design and development of modern cloud DWH & data platforms
- Data Vault automation
- Implementation of ELT and CI/CD processes
- Requirements analysis and data modeling
- Building an automated cloud data platform as a reference architecture for financial risk controlling (Snowflake, Data Vault, DBT, Python, GitHub) 2024-10-01 – 2025-06-30, Zurich
- DWH further development, operations and cloud migration (Data Vault, DBT, SAP Data Services, Alteryx, SQL Server, Azure Synapse) 2023-03-01 – 2025-06-30, Frankfurt
- Implementation of a global cloud data platform (Data Vault, WhereScape, Snowflake, AWS, Scrum) 2021-04-01 – 2023-12-31, Cologne
- Proof of concept for a global cloud data platform (Data Vault, Snowflake, Synapse, WhereScape, Azure, Scrum) 2022-04-01 – 2022-07-31, Bonn
- Implementation of a cloud data platform (Data Vault, Snowflake, WhereScape, AWS) 2021-07-01 – 2022-04-30, Karlsruhe
- Big data integration of all source systems related to the ITSM process (Data Vault, Snowflake, WhereScape, AWS, Scrum) 2021-01-01 – 2021-05-31, Prague
- Development and operation of a global self-service BI platform to display around 150 corporate KPIs (Data Vault, WhereScape, Postgres, Jenkins, Talend, AWS, Scrum) 2018-11-01 – 2020-12-31, Frankfurt
- Implementation of a DWH for price management and capacity forecasting in long-distance passenger transport (SAP BODS, SQL Server, AWS) 2017-10-01 – 2018-11-30, Frankfurt
- Introduction of SAP BODS and migration of the existing DWH (SAP, BODS, HANA, Oracle, Cognos) 2017-05-01 – 2017-10-31, Rastatt
Peka C.
Last position:
Data Warehouse Project for a Zoo at Alfatraining
- Created a complete entity-relationship model (ERM) for the future operational database
- Implemented the model using an RDBMS
- Designed and implemented a star schema for inventory management
Discover over 15,000 top freelancers
Statistics of experts using Data Vault
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
2 years
Positions per freelancer
15
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Banking and Finance, Information Technology, Automotive
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
83%
Master's degree or higher
50%
Certifications per freelancer
6
Most common languages
German, English, French
Speak two or more languages
100%
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 Frankfurt 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 Frankfurt 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 29 Oct 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.
- Banking and Finance (83%)
- Information Technology (83%)
- Automotive (50%)
- Energy (50%)
- Insurance (50%)
- Transportation (50%)
- Retail (50%)
- Telecommunication (50%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Vault does
Data Vault is a data warehousing methodology for integrating data from changing source systems while preserving history and auditability. It separates business keys, relationships and descriptive attributes into hubs, links and satellites. This structure supports traceable enterprise data without forcing every source into one rigid model.
Core model and patterns
A Data Vault solution uses hubs for durable business keys, links for relationships and satellites for context that changes over time. Raw Vault layers retain source-aligned history, while Business Vault layers add rules, derived data and reusable business logic. Strong specialists also apply hash keys, record tracking and load-date conventions consistently.
Ecosystem and tooling
Data Vault work spans warehouse design, ingestion, orchestration and data quality. Specialists commonly connect it with cloud platforms such as Snowflake, Databricks, Microsoft Fabric or BigQuery, alongside SQL, dbt, Airflow and CI/CD workflows. The right tooling depends on source volume, governance needs, latency and the target reporting stack.
Where companies use it
Companies bring Data Vault into environments where many systems must contribute reliable, historised data:
- Enterprise data warehouses and lakehouse foundations
- Finance, supply chain and customer data integration
- Regulatory traceability and audit-ready reporting
- Mergers, acquisitions and changing source landscapes
- Analytics platforms that need repeatable loading patterns
When freelance expertise helps
Freelance professionals are valuable when a team is establishing a new warehouse, modernising a legacy model or resolving inconsistent source data. In Frankfurt, they may support financial, industrial, logistics and professional-services organisations through remote delivery or on-site workshops. They can define modelling standards, build pipelines, review architecture and transfer knowledge to internal teams.
What strong specialists deliver
Quality depends on more than knowing the hub-link-satellite pattern. Strong professionals understand business keys, source-system behaviour, incremental loading, automation and dimensional consumption. They document assumptions, test reconciliation, protect lineage and explain trade-offs clearly to both data teams and business stakeholders. They also know when Data Vault is unnecessary and can recommend a simpler design.
Frequently asked questions
Before you brief your next project: the most common questions about Data Vault.
Data Vault is used to build adaptable enterprise data warehouses and lakehouse foundations that integrate multiple source systems. It preserves historical changes, supports audit trails and allows new sources or attributes to be added with limited disruption.
Data Vault separates integration and history from business-facing presentation, while dimensional modelling focuses on usable facts and dimensions for reporting. Many teams use both: Data Vault as a governed integration layer and star schemas or semantic models for analytics.
Data Vault work benefits from strong SQL, data warehousing, ELT, orchestration and cloud platform skills. Experience with dbt, Airflow, Snowflake, Databricks, Microsoft Fabric, data quality and CI/CD is also useful when the specialist must deliver a complete production pipeline.
A Data Vault engagement needs a specialist who can assess source systems, define business keys and choose appropriate loading patterns for the actual landscape. The right level of experience depends on integration complexity, governance expectations and whether the work covers modelling only or a complete operating platform.
Data Vault projects can usually be delivered remotely because modelling, pipeline development and documentation use collaborative tools. On-site workshops in Frankfurt can still help with source-system discovery, stakeholder alignment and decisions about business definitions; German or English communication should match the project team.
Data Vault may add unnecessary structure for a small, stable data domain with few sources and limited history requirements. A simpler dimensional or relational design can be more appropriate when rapid reporting matters more than broad integration, lineage and long-term change tolerance.
Ask a Data Vault specialist to explain how they identify durable business keys, handle late-arriving data and separate Raw Vault from Business Vault logic. Review their approach to lineage, reconciliation, automated testing, naming standards and consumption models rather than judging only by familiarity with terminology.
A Data Vault professional should clarify the source systems, warehouse or lakehouse technology, orchestration approach, delivery scope and ownership of business rules. They should also establish how stakeholders define data quality, who approves keys and relationships, and whether collaboration is remote, hybrid or on-site in Frankfurt.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Vault in their recent projects is 112 €, which corresponds to a daily rate of about 896 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Data Vault in their recent projects, 83% hold at least a Bachelor's degree and 50% hold at least a Master's degree.
On average, freelancers in Frankfurt, Germany who have used Data Vault in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Frankfurt, Germany who have used Data Vault in their recent projects are German (100%), English (100%), and French (50%).
The most common industries among freelancers in Frankfurt, Germany who have used Data Vault in their recent projects are Banking and Finance (83%), Information Technology (83%), and Automotive (50%).
The most common business areas among freelancers in Frankfurt, Germany who have used Data Vault in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (83%).
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.
Countries:
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
