Data Vault Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, 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.
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.
Ulm Paunel
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 Kisalita
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 Groh
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 Carmel
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 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 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 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 warehousing approach for building audit-friendly, scalable models from changing source systems. It uses hubs, links and satellites to separate business keys, relationships and history. Many companies also look for Data Vault 2.0 skills when they want modern patterns and automation.
Where it fits
- Enterprise data warehouses and data lakes
- Historical tracking across many source systems
- Regulatory and audit-heavy reporting
- Merging data after acquisitions or system changes
It is often chosen when classic star schemas become hard to maintain under frequent change.
Typical deliverables
A strong specialist turns business rules into a clear vault model, sets naming and loading standards, and documents source-to-target logic. They also help with incremental loading, historization, and downstream marts. In Frankfurt, this often matters for finance, insurance and other data-heavy teams that need controlled change.
Ecosystem and tooling
Data Vault work usually sits alongside SQL, dbt, ETL or ELT tools, orchestration, and warehouse platforms such as Snowflake, Azure Synapse, BigQuery or Oracle. The best professionals understand how the vault layer feeds reporting layers without losing lineage. They also know when automation helps and when custom logic is safer.
When to bring in help
- A warehouse needs restructuring and the current model is brittle
- Source systems change often and history must stay intact
- Teams need help with standards, patterns and load design
- Existing marts are inconsistent or hard to audit
Freelance expertise is useful for design reviews, implementation sprints, rescue work and mentoring internal teams.
What strong experts do
Strong Data Vault professionals think in business keys, relationships and traceable history, not just tables. They write clean loading logic, handle edge cases, and keep models understandable for downstream teams. They also communicate clearly with analysts, data engineers and business stakeholders.
Frequently asked questions
Before you brief your next project: the most common questions about Data Vault.
Data Vault is used to build a warehouse layer that keeps history, source traceability and change under control. It is a good fit when many systems feed the same core data and the model must stay stable while sources evolve. Companies use it for reporting, audit needs and long-term integration work.
Data Vault focuses on storing raw business facts, relationships and history in a flexible model. A star schema is usually built for fast analytics and simpler reporting. Many teams use Data Vault as the integration layer and then publish marts or stars on top of it.
Data Vault 2.0 is often the better choice when source systems change often, history must be preserved and lineage matters. It adds modern practices around automation, standards and scalable loading. If the warehouse must support many domains over time, it can reduce redesign work.
A strong Data Vault specialist should know SQL, dimensional modeling basics, warehouse loading patterns and data quality handling. Experience with orchestration, dbt, ETL or ELT tools is also valuable. They should be able to explain why a hub, link or satellite is the right choice for a specific business case.
A Data Vault project benefits from someone who has already built or reviewed real warehouse models, not just read the theory. For a small design review, a focused specialist can help quickly. For a full rollout, you usually want someone who has handled source mapping, historization and downstream consumption.
Yes, Data Vault work is well suited to remote collaboration because most tasks are design, documentation and SQL-based implementation. For Frankfurt teams, a mix of remote work and on-site workshops often works well when business keys, source rules or governance need alignment. Good communication matters more than location.
Look for clear modeling choices, clean source-to-target logic and consistent naming. A strong Data Vault expert can explain trade-offs, show how history is preserved, and connect the vault layer to reporting needs. Good signs are practical examples, solid documentation and calm handling of messy source data.
Data Vault is the original modeling approach, while Data Vault 2.0 is the newer set of methods and supporting practices. People often search for both names when looking for help. In practice, many projects ask for Data Vault skills but expect familiarity with the 2.0 patterns as well.
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
