Data Warehouse Experts in Cologne
, matched with vetted and available freelancers in minutesHire experts who design scalable analytical data models, build reliable ELT pipelines and work with platforms such as Snowflake, BigQuery and Amazon Redshift. FRATCH matches you quickly and precisely with vetted, available freelancers for your project.
Meet FRATCH Experts in Cologne, who have recently used Data Warehouse
Alexander B.
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 L.
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 T.
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 R.
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 B.
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 K.
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 S.
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 W.
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 W.
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 M.
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: 3.2 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: 89%)
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 19 Sep 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Warehouse 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 (80%)
- Banking and Finance (70%)
- Manufacturing (60%)
- Chemical (50%)
- Education (50%)
- Insurance (50%)
- Transportation (50%)
- Professional Services (50%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Analytical foundations
A data warehouse centralizes structured information from operational systems so companies can analyze performance with consistent definitions. It supports reporting, dashboards, forecasting and data products without placing the same workload on transactional applications. A well-designed warehouse creates a trusted layer between source systems and business decisions.
Modern architecture
Professionals choose architectures based on data volume, latency, governance and team capabilities. Cloud data warehouses such as Snowflake, Google BigQuery and Amazon Redshift are common, while some organizations combine warehouses with data lakes or lakehouses. Core work includes dimensional modeling, fact and dimension design, partitioning, clustering and workload management.
Pipelines and tooling
Reliable ingestion and transformation determine whether warehouse data can be trusted. Specialists connect ERP, CRM, product, finance and operational sources, then automate testing, documentation and deployment.
- Build batch and near-real-time ELT pipelines
- Transform data with SQL and dbt
- Orchestrate workflows with Airflow or comparable tools
- Monitor freshness, failures and data quality
When expertise matters
Companies bring in freelance specialists when a warehouse must be created, migrated or made dependable. They can help when reports disagree, pipelines break silently, cloud costs grow, or analysts cannot trace metrics back to their sources. Cologne companies often combine remote delivery with on-site workshops for stakeholders across retail, logistics, manufacturing, media and services.
- Replace fragile spreadsheet reporting
- Migrate from an on-premises warehouse to the cloud
- Establish a governed semantic layer
- Prepare data for machine learning and advanced analytics
Skills beyond SQL
Strong professionals understand more than query writing. They connect business definitions with source-system behavior, model data for actual reporting needs and work comfortably with version control, CI/CD, infrastructure configuration and observability. They also know how to manage access, lineage, retention and personally identifiable information across the warehouse lifecycle.
What quality looks like
A capable specialist delivers understandable models, repeatable pipelines and documentation that another team can maintain. They validate assumptions with business owners, measure freshness and completeness, and design for failure rather than hiding it. Clear trade-offs matter: the right solution balances performance, governance, usability and operating cost for the company’s context.
Frequently asked questions
Before you brief your next project: the most common questions about Data Warehouse.
A data warehouse combines information from systems such as CRM, ERP, ecommerce and finance applications for analysis. Companies use it for consistent reporting, dashboards, forecasting, financial control and data products. It is optimized for analytical queries rather than day-to-day transaction processing.
A data warehouse usually stores curated, structured data with defined business meaning and predictable access patterns. A data lake can hold raw data in many formats, while a lakehouse combines lake storage with warehouse-style management and analytics. The right choice depends on data variety, governance needs, latency and the skills available to the team.
A strong data warehouse specialist often works with Snowflake, Google BigQuery, Amazon Redshift or Microsoft Fabric, depending on the environment. Adjacent skills commonly include SQL, dbt, Airflow, Python, cloud storage, Git and CI/CD. Knowledge of data quality, lineage, security and BI tools is also valuable.
The answer depends on scope, source-system complexity and governance requirements. For a data warehouse initiative, look for a specialist who has delivered comparable models and pipelines, not only someone who knows the selected platform. They should be able to explain design decisions, migration risks, testing and ownership after handover.
Yes. Data warehouse work is often well suited to remote collaboration because development, reviews and cloud environments are accessible online. Cologne-based teams may still prefer on-site workshops for discovery, stakeholder alignment or sensitive migration planning, while delivery remains remote. Clear documentation and agreed communication routines are essential.
Ask how the specialist would model a real business process, test transformations and investigate a conflicting metric. A good data warehouse professional discusses lineage, data contracts, monitoring, access control and recovery plans instead of focusing only on query speed. Request examples of maintainable deliverables and lessons from difficult migrations.
A data warehouse freelancer may deliver the target architecture, source mappings, dimensional or semantic models, ingestion pipelines, transformation code and automated tests. They can also provide orchestration, monitoring, documentation and deployment processes. The precise deliverables should be agreed around the company’s reporting and operational priorities.
An enterprise data warehouse, often shortened to EDW, remains relevant when a company needs governed, shared definitions across departments. Cloud services change how the warehouse is operated, but they do not remove the need for modeling, ownership, quality controls and access policies. Modern teams may use a cloud data warehouse as the central analytical layer within a broader data platform.
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
