
Data Warehouse Experts in Frankfurt
matched in minutes from over 15,000 CVsHire experts who design reliable analytical data models, build ELT pipelines with tools such as dbt and Airflow, and connect cloud data warehouse services to reporting environments. FRATCH finds vetted, available freelancers whose skills match your project precisely.
Meet FRATCH Experts in Frankfurt, who have recently used Data Warehouse
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.
Eric B.
Last position:
Quality Assurance Manager (QSV) at Federal Employment Agency
Supporting the International Web Presence of the BA (IntWeb) project in the area of quality management, with the aim of taking responsibility for the quality of processes and project results while considering BA standards. The main objectives of the project are to give professionals abroad a quick overview of their opportunities for moving to Germany and to enable them to take the necessary steps consistently and with digital support by providing access to services.
Defining the general guidelines in the QA handbook
Summarising test results in QA reports for PLA
Analysing project results for improvement opportunities
Quality management of requirements analysis (particularly processes, methods, and tools)
Ensuring compliance with SERA requirements
Creating a cross-project test concept
Agreeing on sprint completion reports
Formal review of work results in accordance with requirements and/or the project plan
Contact person for internal audit, external audits by auditors, or BRH (Federal Court of Audit)
Technologies: JIRA, Confluence, MS Office, GitLab, Kubernetes
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.
Monika T.
Last position:
Senior ETL Lead at Takeda GmbH
- Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
- Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
- Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
- Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
- Designed scalable data foundations suitable for downstream analytics and AI workloads.
- Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.
Mustafa K.
Last position:
Senior Network Design and Engineer at Helaba
Designed and specified the technical network integration of a new business critical application system (MUREX) in the capital market area in a complex interface and outsourcing environment.
Created component specifications, catalog of measures and implementation plan.
Ensured proper integration of developed results in accordance with the bank’s framework: IT compliance, IT security, change and release management.
Selected and integrated a new service provider for the bank; moved critical bank applications to the provider.
Designed, implemented and supported the new Cisco network and Fortinet security setup at the service provider data center.
Acted as technical interface between bank, service providers and consulting partners.
Supported and further developed the network and security infrastructure (clients, servers, networks); performed error analysis and implemented solutions within agreed service levels.
Analyzed and diagnosed all security and network failures, glitches and malfunctions.
Initiated continuous enhancements to guarantee efficient resource utilization and acceptable response times to users.
Provided network support in financial service organization and operational change preparation on production banking network infrastructure for both large and small projects within strict change management discipline.
Designed, defined, tested, governed and improved engineering standards.
Performed role of network architect for medium-large projects involving team resources.
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.
Werner K.
Last position:
Test Coordinator, Designer and Engineer at IBM
- Testing the SekIDP and related components
- Test design, execution and automation, microservices, GitHub Enterprise, Eclipse, Katalon Test Platform, API Testing, Confluence 8, JIRA/Xray, Draw.io, Swagger/OpenAPI, Postman, Docker, Kubernetes, Podman, PuTTY, E-Health, Telematik, Gematik standards, EPA, E-Rezept, sektoraler IDP, encryption, XaDES, PaDES, DICOM, HL7, FHIR, IHE, ICD, SSO, SAML/Shibboleth, OAuth, smartcards, JWT, two factor authentication Android and iOS, Wireshark, BrowserStack, Cypress, gRPC, REST, SOAP, WS-Security, Linux Shell, PowerShell, SSH/SSL, Java, Kotlin, Cordova, Gradle, Groovy, Python, TypeScript
Waleri M.
Last position:
Project Manager at WAMOCON Academy
- Project planning and resource planning
- Requirements definition and technology selection
- Data migration and risk management
- Monitoring up to go-live
- Tools: Office365, Jira XRAY, Strato, Onboarding App
Judith B.
Last position:
BI & Analytics Consulting
- Analysis of web and campaign performance to derive actionable insights for marketing and growth optimization
- Implementation and maintenance of tag management solutions to ensure reliable and consistent data collection
- Continuous development and optimization of reporting structures with a focus on scalability and data quality
- Conducting regular deep-dive analyses and leading monthly stakeholder sessions to present results and align on optimization measures
- Design and operation of end-to-end data flows from data collection to visualization
- Tools: GA4, Google Tag Manager, Looker Studio, Airbyte, BigQuery
Peter W.
Last position:
ISO 27001 Auditor for health insurance archive system at Health insurance company
The replacement of the existing archive system (document management system – DMS) on a host-based platform is well advanced.
The internal audit is meant to ensure the company's quality standards.
GDPR
ISO 27001 ff.
BSI
DORA
Patient data regulations
Host / Cloud / S3 / Container / highly scalable / Nuxeo
Budget: 50,000
Team: 1
Kamran V.
Last position:
Agile Delivery Manager at Sparecodes Incorporation
- Highly successful role as lead data analytics expert at a US consulting startup.
- Managed stakeholder expectations by ensuring team delivery velocity using Agile and Lean practices.
- Trained and assembled international offshore data science teams of DevOps engineers, BI developers, and technical support specialists.
- As Sparecodes' data analytics experts, we improved our clients’ business outcomes through requirements management, dashboard and report development, migration, automation, and production support using IBM Cognos, AWS, SAP, Oracle, Teradata, CRM, ERP, Confluence, and Microsoft.
- Provided effective coaching and mentoring for international, multicultural teams in a remote environment, covering new tools and techniques, Agile best practices, internal and external communication, and presentation skills.
- Coordinated technical processes efficiently, including managing and implementing project changes and control processes.
- Promoted strong team engagement and supported employees’ career development, including business change management through coaching.
- Enhanced project status reports by providing insights into team productivity, predictability, and backlog burn-down, leading to improved project outcomes.
- Successfully led the coordination and implementation of software solutions for clients in mechanical engineering, including automation system integration and customization to meet client needs.
Marie-Josée M.
Last position:
Java Engineer / Developer at Davaso GmbH
- Involved in the full development lifecycle, including design, coding, testing, and deployment of Java-based applications.
- Built and maintained high-performance RESTful APIs and microservices, ensuring scalability and reliable data exchange.
- Practiced code reviews, used version control systems (e.g., Git), and collaborated closely with cross-functional teams to deliver robust software solutions.
- Implemented, managed, and optimized complex business logic and decision-making processes using the Drools Rules Engine, leveraging DRL (Drools Rule Language) for dynamic configuration.
- Developed software solutions for a major service provider in the healthcare insurance sector, specifically focusing on claims processing and reconciliation logic to ensure cost-effective reimbursement for health funds.
Kabir K.
Last position:
AI Engineer / Banking IT Specialist at Hamburg Commercial Bank (HCOB) & Real Estate Firm
- Developed a retrieval-augmented generation (RAG) application using LangChain and LangGraph for corporate document parsing, delivered as an installable Electron desktop application with local AI models via Ollama.
- Currently providing ongoing AI feature support for the Loan Pricing Tool at Hamburg Commercial Bank, with a commitment of three days per month.
- Architected Kubernetes-native solutions, including Helm chart configuration and Azure DevOps pipeline integration.
Andreas S.
Last position:
Project Management, Development and Test- (Defect-) Management at safarica.com
- Creation and implementation of a multilingual, relational MariaDB database to reflect the requirements of a booking system designed for safaris in Africa
- Testing the front- and backend functionality, registration of clients and suppliers, the editorial system and the payment workflow
- Defect management and documentation via BugHerd
- Implementation of an automatic internal deployment of deliveries provided by an external company via bash scripts
- Coordination of external companies in the function of a project manager (Stakeholder Management)
Nauman R.
Last position:
IT Product Group Manager / Senior PMO at DB Fernverkehr AG
- Planning, management and execution of six integration projects for the in-vehicle passenger information system
- Agile software development using Scrum
- Full responsibility for quality, schedule, scope and budget of the integration projects
- Supporting further development of product groups and services (Wi-Fi & network) and implementing new features
- Preparing and updating change requests and releases in JIRA, and reviewing supplier and integration lab tests
Discover over 15,000 top freelancers
Statistics of experts using Data Warehouse
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 22 years)

Position duration
2.5 years (Germany: 3.2 years)

Positions per freelancer
13

Top business areas
Information Technology, Business Intelligence, Project Management

Top industries
Banking and Finance, Information Technology, Professional Services

Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
83% (Germany: 89%)
Master's degree or higher
48% (Germany: 59%)
Doctorate
7% (Germany: 10%)

Certifications per freelancer
4 (Germany: 3)

Most common languages
German, English, French

Speak two or more languages
100% (Germany: 95%)
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 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 29 Oct 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.
- Banking and Finance (74%)
- Information Technology (71%)
- Professional Services (43%)
- Healthcare (37%)
- Insurance (37%)
- Transportation (31%)
- Telecommunication (31%)
- Energy (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Analytical foundation
A data warehouse centralizes structured data from operational systems so companies can analyze it consistently. It supports reporting, dashboards, financial analysis, forecasting and decision-making without putting the same load on transactional applications. Teams may use a traditional enterprise data warehouse, a cloud data warehouse or a wider lakehouse architecture depending on their data and governance needs.
Models and pipelines
Strong warehouse work starts with clear business definitions and dependable data flows. Specialists design dimensional models, fact and dimension tables, slowly changing dimensions and appropriate layers for raw, prepared and reporting data. They build batch or near-real-time ELT pipelines, handle schema changes, and make failed loads visible and recoverable.
- Map sources, owners and business definitions
- Create transformation logic with SQL and dbt
- Schedule workflows with Airflow or comparable tools
- Add tests for freshness, uniqueness and referential integrity
Ecosystem and platforms
The right professional understands both warehouse concepts and the surrounding toolchain. Common environments include Snowflake, Google BigQuery, Amazon Redshift, Microsoft Fabric and Azure Synapse, alongside SQL, Python, dbt, Airflow, Kafka and BI tools such as Power BI or Tableau. Experience with cloud storage, orchestration, catalogs, access controls and observability matters as much as platform syntax.
When expertise matters
Companies often bring in freelance specialists when reporting has become inconsistent, a migration is underway, or an existing warehouse cannot support new data products. Frankfurt-based teams may value on-site workshops for stakeholder alignment, while remote delivery can work well for implementation, documentation and review. Clear communication in English or German should match the project’s working model.
- Replace spreadsheet-heavy reporting with governed models
- Migrate workloads between warehouse platforms
- Improve slow queries and uncontrolled compute use
- Prepare trusted datasets for finance, sales or operations
Quality signals
A capable professional asks how metrics are defined before writing transformations. They can explain lineage from source to dashboard, separate business logic from infrastructure, and show how tests, monitoring and access policies protect the result. They also document assumptions, ownership and recovery steps so the warehouse remains maintainable after handover.
Delivery and collaboration
Good engagements have a clear source inventory, target architecture, priority use cases and acceptance criteria. The specialist should leave behind version-controlled SQL, repeatable deployments, tested pipelines and practical documentation. For Frankfurt projects, agree early on workshop attendance, remote communication, data access boundaries and the language used with business stakeholders.
Frequently asked questions
What clients ask us most about Data Warehouse — answered in short.
A Data Warehouse brings data from systems such as finance, CRM, ERP and web analytics into a structure built for analysis. It supports consistent reporting, dashboards, trend analysis, forecasting and controlled data access.
A data warehouse usually stores curated, structured data optimized for trusted queries and business reporting. A data lake keeps a wider range of raw files, while a lakehouse combines lake storage with warehouse-style management and analytics features.
A strong Data Warehouse specialist should understand SQL, data modeling, ELT, orchestration and cloud infrastructure. Skills with dbt, Airflow, Python, data quality testing, metadata, security and BI tools are also valuable.
A Data Warehouse engagement may cover discovery, architecture, modeling, pipeline delivery, migration or performance work. The required depth depends on source complexity, governance needs, platform choice and whether the specialist must also support reporting and handover.
Yes, Data Warehouse work is often suitable for remote collaboration because code, models, documentation and infrastructure can be reviewed online. On-site sessions in Frankfurt can still help with source-system discovery, stakeholder workshops and decisions about sensitive data access.
Ask a Data Warehouse specialist to explain a past model from source system to final report, including tests, lineage and failure handling. Strong answers show clear metric definitions, maintainable SQL, controlled access and a practical approach to documentation.
A Data Warehouse freelancer may specialize in Snowflake, Google BigQuery, Amazon Redshift, Microsoft Fabric or Azure Synapse. Platform experience should be matched with the company’s cloud environment, workload pattern, security model and existing orchestration tools.
Before joining a Data Warehouse project, clarify source ownership, business definitions, data quality, delivery milestones and access restrictions. Also confirm whether the work is focused on a new build, migration, modernization, optimization or ongoing operational support.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Warehouse in their recent projects is 104 €, which corresponds to a daily rate of about 830 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Data Warehouse in their recent projects, 83% hold at least a Bachelor's degree, 48% hold at least a Master's degree, and 7% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Data Warehouse in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Frankfurt, Germany who have used Data Warehouse in their recent projects are German (100%), English (97%), and French (23%).
The most common industries among freelancers in Frankfurt, Germany who have used Data Warehouse in their recent projects are Banking and Finance (74%), Information Technology (71%), and Professional Services (43%).
The most common business areas among freelancers in Frankfurt, Germany who have used Data Warehouse in their recent projects are Information Technology (100%), Business Intelligence (83%), and Project Management (71%).
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.
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!

Berlin
Hamburg
Munich
Cologne
Stuttgart
Dusseldorf
Dortmund
Essen
Hanover
Nuremberg