Data Warehouse Experts in Cologne
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Meet FRATCH Experts in Cologne, who have recently used Data Warehouse
Alexander Bromberg
Last position:
Senior Data Engineer at RWE AG
Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.
Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows
Rainer Langbehn
Last position:
Senior IT Consultant, Senior Software Architect, Senior Software Developer, Senior DevOps Engineer at Techniker Krankenkasse
Automation of the database major / minor releases for the TKeasy project
Concept for database major / minor release automation
As-is analysis
Evaluation of Redgate Flyway functionality
Concept creation
Products: Redgate Flyway, GitHub, Quest, Erwin Data Modeller, Oracle, Atlassian Jira, Atlassian Confluence
Skills: Docker, Continuous Delivery, Continuous Integration, Redgate Flyway, Major Releases, Minor Releases
Emmanouil Tzouridis
Last position:
Senior Analytics Engineer at Trade Republic Bank GmbH
- Implementation of analytics and automation solutions for the Anti Financial Crime business unit
- Providing the infrastructure, including reusable data models and feature ingestion for production ML and rule based models in the areas of Account Take-Over and Card fraud detection, as well as Customer Risk Assessment
- Tools used: Snowflake, dbt, Looker, AWS, Python, Airflow, Metaflow
Thomas Reick
Last position:
BI Consultant and Power BI Developer at Plusnet GmbH
- In-depth forensics of old and new data sources
- Enhancing existing reports and creating new data analyses
- Data preparation from source systems and semantic models
- Azure DevOps integration
- Tools/Skills: MS-PowerBI, DAX, MS-Office 365, PowerQuery, M, PowerAutomate, Redmine, SharePoint, SQL (T-SQL, Oracle), MS Fabric
Peter Brungs
Last position:
Data Warehouse Consultant (Development and Analysis) at Atruvia AG
- Developed and enhanced ETL loading jobs with IBM DataStage and optimized SQL in an IBM DB2 environment as part of the Agree21 data migration
- Analyzed data quality and developed test procedures
- Created PowerShell scripts and documented GIT deployment processes
- Technologies: RedHat Linux, IBM DB2 with DBVisualizer, IBM InfoSphere DataStage 11.7, JIRA, TortoiseGIT, TortoiseSVN, PowerShell scripts
Denis Kirpicev
Last position:
Management Consultant at Freelance Management Consultant
Implementation of custom reporting solutions for financial KPIs aligned with specific business requirements
Development of a machine learning application that achieved a 250% performance improvement
Data Architect "Production-Oriented Quality Assurance" (03/2024–09/2024):
Design and implementation of an analytics platform to detect quality deviations in manufacturing
Build of a scalable data lakehouse architecture on Databricks in combination with SAP ERP data via SAP Datasphere
Close collaboration with the SAP team to harmonize bill of materials and order data
Visualization of KPIs to support shopfloor management
Lead Data Engineer "Sales Performance Monitoring" (08/2023–12/2023):
Design and implementation of a Databricks-based platform for analyzing sales figures and promotion effects
Integration of SAP SD data via SAP BW/4HANA
Use of Azure DevOps to orchestrate ETL jobs and deploy workflows
Technical Project Lead "Cloud Migration & Data Strategy" (02/2023–05/2023):
Migration of a heterogeneous data warehouse stack to a modern cloud architecture on Azure with Databricks as the central processing platform
Development of a governance-compliant data architecture to integrate SAP financial data and non-SAP sources
Technologies: Databricks, Delta Lake, Python, SAP Datasphere, Azure DevOps, PowerBI, SQL
Giovanni Spinelli Barrile
Last position:
Technical Product Manager at Logicc GmbH
Acted as the primary bridge between Legal, Engineering, and Business units to ensure zero compliance violations while maintaining product velocity.
Led the development of a GDPR-compliant AI aggregator platform, managing a roadmap that balances legal constraints with aggressive feature delivery.
Scaled the engineering team from 4 to 9 developers, establishing hiring protocols and technical onboarding processes to support rapid product iteration.
Boosted the development process by introducing structured sprint cycles and backlog refinement, resulting in a 20% reduction in feature delivery time.
Architected and prototyped agentic AI workflows with n8n and RAG pipelines on Langchain.
Dieter Wagner
Last position:
Freelancer at Morat Swoboda Motion GmbH
- Manual for the new cost center structure in SAP and the new posting procedures
- Plant controlling, operating cost sheets, and inventory valuation
- Rate determination in Excel for SAP
- Preparation of product calculations
Peter Weiß
Last position:
Business Analyst at Finanz Informatik GmbH & Co. KG
- Maintenance and further development of the OSPlus software in the cashier area
- Use of IBM Notes and HP Quality Center
Robert Meixner
Last position:
Air traffic tax German Customs Administration SOAP Webservices Client Requests at Team GmbH
In the citizens' and business customer portal of the Customs Administration, airlines submit applications for departures and landings every month
These applications are forwarded to the caseworkers via SOAP web service and, after review and processing, any queries or notifications are returned via SOAP web service
PL/SQL programming of SOAP webservices client requests for HTTP/XML message exchange between the BuG portal and the caseworkers
New creation and change requests for Forms and Reports 12 modules
Forms applications start and read SOAP webservice requests via PL/SQL packages, process them and send Reports 12 documents to the airlines
Discover over 15,000 top freelancers
Statistics of experts using Data Warehouse
Aggregated from the professional profiles of matched freelancers.
Experience
18 years (Germany: 22 years)
Position duration
1.9 years (Germany: 3.3 years)
Positions per freelancer
13
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Banking and Finance, Manufacturing
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
80% (Germany: 88%)
Master's degree or higher
60% (Germany: 57%)
Certifications per freelancer
4 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
90% (Germany: 95%)
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 Cologne 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 Cologne using Data Warehouse
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
What it does
A data warehouse brings data from many systems into one place for analysis. Teams use it for management reporting, KPI tracking, finance close support, customer insight, and operational dashboards. It is often called a DWH or enterprise data warehouse.
Common stack
- Source system integration and change capture
- SQL modeling for fact and dimension tables
- ETL and ELT pipelines
- BI tools, semantic layers, and data quality checks
Strong experts know how these parts fit together and how to keep them reliable as the business grows.
When to bring in help
Companies bring in freelance specialists when a warehouse project needs a clear data model, better load performance, or a redesign after fast growth. They also help when reporting is inconsistent across teams or when existing pipelines are hard to maintain. In Cologne, this often matters for firms that need close work with local stakeholders and can mix on-site sessions with remote delivery.
What good work looks like
Good professionals think in business questions, not only tables and jobs. They define source-to-target mappings, design clean grain, handle history correctly, and document assumptions so analysts can trust the numbers. They also spot weak joins, duplicate logic, and unclear ownership early.
Ecosystem around it
Data warehouse work often touches SQL, dbt, Snowflake, BigQuery, Redshift, SQL Server, Oracle, and Databricks. The exact mix depends on cloud strategy, data volume, governance rules, and the BI layer in use. Many projects also need security, access control, lineage, and testing conventions.
Typical outcomes
A strong specialist leaves behind more than loaded tables. Expect stable reporting models, clear naming, scheduled refreshes, reusable transformations, and documentation that helps both technical and business teams. That makes future change safer and faster.
Frequently asked questions
Before you brief your next project: the most common questions about Data Warehouse.
A Data Warehouse stores integrated data for analysis and reporting, not for day-to-day transaction processing. Companies use it for dashboards, finance reporting, customer analysis, and a shared view of business performance. It helps turn scattered source systems into consistent metrics.
Bring in a data warehouse specialist when you need to build a new analytical layer, fix slow or brittle pipelines, or clean up reporting logic after growth. Freelancers are also useful when internal teams need short-term help on modeling, migration, or quality checks. They can join for design work, delivery, or a rescue effort.
A Data Warehouse is shaped for structured analysis and trusted reporting, with clear models and defined business rules. A data lake is broader and often keeps raw or semi-structured data for many uses. Many companies use both, but the warehouse is usually the source for governed BI.
A strong data warehouse professional usually knows SQL, dimensional modeling, ETL or ELT design, and at least one modern platform such as Snowflake, BigQuery, or Redshift. Data quality, access control, and documentation matter too. If the project uses dbt or a BI tool, that experience is a clear plus.
For a simple reporting layer, one capable Data Warehouse expert may be enough. For migrations, multiple source systems, or strict governance, you usually want someone who has handled end-to-end design and delivery before. The more systems and stakeholders involved, the more valuable deep warehouse experience becomes.
Yes. Most data warehouse work can be done remotely if the specialist has secure access to source systems, a clear data owner, and regular feedback from the business. In Cologne, many teams prefer a mix of remote work and in-person workshops for model design and stakeholder alignment.
Ask for a recent Data Warehouse case that shows source systems, model design, testing, and how the final reporting layer was used. Good experts explain trade-offs clearly and do not hide behind tool names. Look for clean naming, practical documentation, and a careful approach to data rules.
SQL is essential, but it is rarely enough for a full data warehouse project. The specialist should also understand the target platform, orchestration, testing, and how analysts consume the data. For migration or optimization work, platform knowledge can be the difference between a working result and a fragile one.
The average hourly rate of freelancers in Cologne, Germany who have used Data Warehouse in their recent projects is 94 €, which corresponds to a daily rate of about 750 € based on an 8-hour working day.
Of the freelancers in Cologne, Germany who have used Data Warehouse in their recent projects, 80% hold at least a Bachelor's degree and 60% hold at least a Master's degree.
On average, freelancers in Cologne, Germany who have used Data Warehouse in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Cologne, Germany who have used Data Warehouse in their recent projects are German (100%), English (90%), and French (20%).
The most common industries among freelancers in Cologne, Germany who have used Data Warehouse in their recent projects are Information Technology (80%), Banking and Finance (70%), and Manufacturing (60%).
The most common business areas among freelancers in Cologne, Germany who have used Data Warehouse in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (70%).
Main locations of FRATCH Experts, who have recently used Data Warehouse
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 
Frankfurt 
Dusseldorf 
Essen 
Hanover 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
