Data Quality Experts in Germany
in minutes from over 15,000 CVs with the power of AI.Hire experts who assess data accuracy, define quality rules, and fix issues in pipelines, warehouses, and master data flows. Work with specialists who help teams keep reporting reliable and catch problems before they spread, with fast, precise matching of vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Data Quality
Fadi Shoaa
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Vadim Romanenko
Last position:
Senior Business Analyst – Digital Products & Risk at Deutsche Landesbank
- Analysis, structuring, and prioritization of complex business and stakeholder requirements for a business-critical limit and risk system.
- Translation of business requirements into backlog items, user stories, acceptance criteria, and end-to-end scenarios.
- Facilitation of structured workshops and refinements with business, development, and QA to clarify complex issues and derive actionable product and system requirements.
- Optimization of requirements, testing, and quality processes, including automation, data quality, and CI/CD-related workflows.
- Discovery and evaluation of AI/GenAI use cases in terms of user and business value, data basis, feasibility, and potential capability impact.
Wolfgang Orgler
Last position:
Business Analyst at DekaBank
Lead Business Analyst – Analysis and optimization of private banking processes
Responsibility for the business analysis and further development of business processes in private banking, with a focus on CRM, customer data management, and master data processes
Carrying out a comprehensive business process analysis to identify optimization potential, business gaps, and improvement opportunities along the customer lifecycle
Gathering, analyzing, and structuring business requirements in close cooperation with business units, management, IT architecture, and development teams
Creating and aligning business concepts, process models, user stories, and requirement documentation as the basis for technical implementation
Analyzing and optimizing existing master data processes with a focus on data quality, responsibilities, and efficient data maintenance
Designing and further developing CRM customer data processes while taking regulatory requirements and business goals into account
Planning and moderating workshops with business, IT, and stakeholders for requirements analysis, process design, and solution finding
Managing requirements and ensuring consistent communication between business and IT
Using AI-supported analysis tools to help with requirements analysis, structuring information, and improving documentation quality
Franz Bauer
Last position:
Product Development (AI) at Own initiative
AI telephone assistant platform
Claude Code, Google AI Studio, Python, LLM / Voice-AI, PostgreSQL
- Conception and hands-on development of an AI-supported telephone assistant platform (voice AI / LLM) – from idea and architecture to MVP/product.
- Built agentic workflows and full automations with Claude Code and Google AI Studio.
- Also delivered AI-supported work in client engagements: used Claude Code for governance documentation, requirement drafts, and automations.
Christine Tantschinez
Last position:
Communications Consulting at Storytrend
Most mid-sized companies already have their numbers. What is missing is the translation: a dashboard with forty tiles does not answer a single question that is actually asked in management.
Analysis
- Evaluation of existing data with Python and SQL
- Checking data quality and methodology before making a statement
- The result is an analysis that leads toward a concrete decision
Preparation
- Reports in Power BI and Tableau
- Interactive calculators and visualizations on the web
- Presentations and specialist texts for customers, sales and the public
- Analysis and communication from one source — that
Qamar Hussain
Last position:
Freelancer at qhconsulting.de
- Building and leading an IT consulting and outsourcing company with a focus on AI, app development, and digital transformation
- Acquiring and supporting B2B customers in Germany, including technical project management with offshore teams in India, Pakistan, and Eastern Europe
- Developing and marketing tailored consulting and training packages in the field of artificial intelligence (including EU AI Act compliance)
- Overall responsibility for business development, strategy, marketing, sales, and partner management
- Running webinars and on-site seminars on AI integration in companies
Folke Von Königslöw
Last position:
Nameling – AI-supported product development
- Relaunch of a self-developed semantic name recommendation product by combining semantic search, graph-based similarity analysis, LLM-/RAG-supported content, and AI-supported development processes.
- End-to-end responsibility in the product lifecycle - from use case definition and solution design to prototyping and evaluation, and then iterative roadmap development.
- Assessment of AI use cases in terms of user value, technical feasibility, data quality, governance, and operating costs to guide MVP scope, roadmap decisions, and continuous product improvement.
Jens Reichert
Last position:
Finance Transformation Director at Bauer Media Group
After several S/4 go-lives, structure, role clarity, and decision clarity were missing in Finance and IT; governance mechanisms, prioritization logic, and responsibilities were not defined well enough.
Unclear interfaces, high coordination effort, and inconsistent ways of working led to organizational instability and limited leadership and steering capability.
The existing operating model between Finance, Controlling, and IT was not working, which affected transparency, collaboration, and decision paths.
Designed the transformation and organization architecture for Finance and clarified roles, decision paths, and priorities.
Diagnosed and structured the Finance and IT organizations (DE/UK/PL), including collaboration, responsibilities, and interfaces.
Redesigned the Finance–IT operating model with a focus on governance, steering routines, and cross-functional collaboration.
Enabled leaders and teams, especially Global Process Owners, Key Users, and Finance Leads (systemic organizational development / leadership).
Steered the transformation portfolio, including clarity on risks, dependencies, and cross-functional decision processes.
Restored structural steering capability by clearly defining roles, decision paths, and priorities and anchoring them across the organization.
Strengthened governance and cross-functional collaboration in a systemic way - Finance, Controlling, and IT worked again in consistent, aligned structures; friction losses dropped measurably.
Harmonized working and communication logic, making coordination faster, more transparent, and less conflict-heavy.
Enabled the organization in a sustainable way, including the build-up of a strong Key User / Process Owner community and clearly defined steering routines.
Made the transformation structurally manageable - prioritized initiatives, clear risk and progress logic, and consistent decision formats increased execution speed.
Hooman Behmanesh
Last position:
Fullstack Developer at Möbel Roller GmbH
- Further development of the existing e-commerce platform based on SAP Commerce (Hybris) to meet the growing demands of digital commerce.
- Ensuring the scalability and performance of the backend, so the platform remained stable and efficient even under heavy user load.
- Development and integration of new OCC REST APIs and services for modular extensions and flexible adjustments, to implement new features quickly.
- Optimization of data flows and interfaces, which significantly improved platform efficiency and system performance.
- Ensuring a maintainable and scalable code base by using Clean Code principles, proven design patterns, and a future-proof architecture.
- Reduction of errors through extensive testing with JUnit, Mockito, and load tests with Gatling, supported by the introduction of automated test processes.
- Improved system performance through targeted refactoring measures and efficient database queries, especially to handle peak loads.
- Use of modern cloud and monitoring tools such as Kubernetes, Google Cloud Platform (GCP), and Grafana to ensure a stable and monitored infrastructure.
- Clear improvement in efficiency, scalability, and reliability of the platform, which now meets the demands of a dynamic and growing e-commerce market.
Loretta Acheampong
Last position:
Master Thesis Student at Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Future Fuels
- Managed end-to-end data workflows for LCA and carbon footprint assessments, including data collection, validation, structuring, and LCI modeling for metal alloys, ensuring high data quality and traceability.
- Developed a basic Excel automation tool to simplify carbon footprint calculations of metal alloys, replacing repetitive modelling in the software that previously took several hours.
- Communicated environmental impact results through quantitative analysis, visualizations and reports to support the broader project strategy.
- Performed environmental hotspot and scenario analysis to identify key impact drivers and assess opportunities for emissions reduction.
Henning Uiterwyk
Last position:
Senior Expert Data Governance, Master Data Quality and Data Migration at E.ON
- Planning and implementation of a migration strategy for master and transaction data for the continuous loading of a cloud-independent database
- Creation and pilot implementation of a company-wide Business Data Model for customers, suppliers, contracts, products, prices, consumption, invoices, and dunning
- Concept and consulting for a Data Governance Framework incl. definition of committees, roles, processes, and metadata model
- Operationalization of the Data Governance Framework with definition of data standards
- Sub-project management in two pilot projects (PoCs) for Data Governance systems (ErwinDIS and Atlan)
- Training and coaching the data team in migration, data quality, and data modeling
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.
Dennis Domanski
Last position:
Interim Senior Manager Accounting at Reliance Trust Luxembourg S.A.
- Operational leadership (Fast Close): Structuring and managing the entire close process for complex German and international client portfolios (Real Estate, Private Equity, HNWI).
- Audit readiness & governance: Ensuring compliance in the timely preparation of annual financial statements and tax returns under IFRS, HGB, and LuxGAAP.Efficient
- Audit support: Successful interface management and coordination with auditors (Big Four), which demonstrably shortened the audit duration.
- Special situations & complexity: Professional leadership of cross-functional teams in clearing and working through historically grown accounting backlogs.
Christian Frauer
Last position:
Department Head (Interim) at Municipal utilities and transport company
- Definition and setup of the subject areas
- Building a governance model for the department with the areas of responsibility
- IT strategy, project management, process management, and quality and sustainability management
- Developing a communication strategy within the group
- Creating the IT strategy
- Designing templates, guidelines, and processes for consistent work
- Capturing strategic guardrails and grouping ongoing projects – deriving a roadmap for strategic planning
- Reviewing ongoing projects
- Creating staffing calculations and volume structure
- Defining job profiles
Markus Blohm
Last position:
Consultant, Technical Project Manager at Lanxess
- Project: ESU Windows and SQL Server consolidation / Configuration Management in ServiceNow
- Creation of an as-is analysis of all servers worldwide
- Creation of an as-is analysis of all SQL servers worldwide
- Creation of requirements analyses for SQL and server migrations
- Coordination of requirements with partners for migrations to Hyper-V (on-premises) or Azure Cloud
- Moderation of jour fixe meetings
- Creation of roadmaps and solution models for server migrations
- Setup of test scenarios
- Moderation of workshops for the introduction of a Global Admin Team
- Creation of data models (CMDB) in ServiceNow
- Conducting workshops for the CMDB data model
- Creation of solution models for the CMDB
- Creation of seamless configuration and work documentation for the CMDB
- Creation of reports for license management
- Creation of KPIs for data quality in ServiceNow
- Creation of a service catalog
- Moderation of workshops for creating service requests
- Interface between needs analysis and ServiceNow development team
- Systems used: Windows 7, Windows 10, Microsoft Office 365/2010, Windows Server 20xx, SQL Server 20xx, SharePoint, Azure Cloud, Hyper-V, ServiceNow, various tools
Discover over 15,000 top freelancers
Statistics of experts using Data Quality
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
3 years
Positions per freelancer
10
Top business areas
Information Technology, Business Intelligence, Project Management
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
96%
Master's degree or higher
68%
Doctorate
11%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
98%
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 Germany 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 Germany using Data Quality
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 covers
Data quality is the discipline of making data fit for use. It focuses on accuracy, completeness, consistency, timeliness, and uniqueness across source systems, warehouses, and operational tools. Strong work in this area supports reporting, analytics, compliance, and automation.
Typical work
- Define quality rules for key fields and records
- Profile datasets to find missing, duplicate, or inconsistent values
- Set up checks for pipelines, dashboards, and master data
- Clean and standardize data before it reaches downstream systems
- Document data quality issues and remediation steps
Common stack
Professionals in this area often work with SQL, Python, dbt, Great Expectations, and data observability tools. They also need a solid grasp of ETL and ELT processes, metadata, lineage, and source-to-target mapping. In many teams, they collaborate closely with data engineering and analytics specialists.
When to bring in help
Companies usually seek freelance expertise when data defects slow down a project, a warehouse migration is underway, or critical reports no longer match across systems. In Germany, this work often sits close to finance, manufacturing, logistics, and regulated environments where reliable data is non-negotiable. Remote work is common, but on-site sessions can help align business rules with local teams.
What good experts do
Strong specialists do more than run checks. They ask where the data comes from, who uses it, and what “correct” means in each process.
- Translate business rules into testable controls
- Trace root causes instead of patching symptoms
- Balance strict rules with practical exceptions
- Work cleanly with data owners and technical teams
Signs you need one
If dashboards conflict, duplicates keep returning, or onboarding new data sources keeps breaking downstream logic, a data quality specialist can help stabilize the flow. Many teams also bring in outside help for data quality management reviews, reference-data cleanup, and governance setup. The right person leaves behind clear rules, not just a one-time fix.
Frequently asked questions
Quick answers to the questions that come up most around Data Quality.
A strong Data Quality specialist finds where data breaks down and puts controls in place to keep it usable. That can include profiling tables, writing validation rules, cleaning duplicates, and documenting what each field should mean. The goal is dependable data for reporting, operations, and analytics.
Data Quality is about the condition of the data and the checks that keep it reliable. Data cleansing is one activity within that work, while data governance is the wider framework of ownership, rules, and accountability. In practice, the three often overlap, but they solve different problems.
A good Data Quality expert usually brings SQL, Python, and experience with warehouses, pipelines, and validation frameworks. dbt, Great Expectations, and data observability tools are common in modern teams. Knowledge of source systems, metadata, and business rules matters just as much as the tool set.
Companies bring in Data Quality expertise when bad records keep spreading, dashboards disagree, or a new source needs to be trusted quickly. It is also common during migrations, mergers, and master data cleanup. External specialists help when the team needs structure, speed, and an outside view.
That depends on the scope of the Data Quality work. A focused rule-set or profiling task may need one specialist, while an enterprise cleanup needs someone who understands systems, ownership, and downstream impact. The best match is not just technical; it is someone who can translate business definitions into controls.
Yes, Data Quality work is often remote because the main tasks are analysis, rule design, and collaboration around data definitions. For teams in Germany, remote experts can work well if they communicate clearly and document decisions carefully. On-site workshops can still help when business rules are disputed or many stakeholders are involved.
Look for a Data Quality specialist who can explain root causes, not just list issues. Good signs are clear test logic, practical remediation plans, and examples of improving data across source, pipeline, and reporting layers. Ask how they define quality, how they measure it, and how they prevent the same issue from returning.
Yes, Data Quality is often shortened to DQ in teams and search queries. You may also see related phrases such as data quality management, data cleansing, or data validation, depending on the context. The right specialist should understand the full workflow, not just one label.
The average hourly rate of freelancers in Germany who have used Data Quality in their recent projects is 99 €, which corresponds to a daily rate of about 796 € based on an 8-hour working day.
Of the freelancers in Germany who have used Data Quality in their recent projects, 96% hold at least a Bachelor's degree, 68% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Germany who have used Data Quality in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Germany who have used Data Quality in their recent projects are German (98%), English (97%), and French (14%).
The most common industries among freelancers in Germany who have used Data Quality in their recent projects are Information Technology (74%), Professional Services (49%), and Banking and Finance (42%).
The most common business areas among freelancers in Germany who have used Data Quality in their recent projects are Information Technology (87%), Business Intelligence (69%), and Project Management (60%).
Main locations of FRATCH Experts, who have recently used Data Quality
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 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Hanover 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
