Data Quality Experts
in minutes from over 15,000 CVs with the power of AIHire experts who clean, validate, monitor, and govern data across databases, pipelines, and analytics stacks, so reports, models, and operations can rely on trusted information. Fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts 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
Wolfgang Döbber
Last position:
Agile Coach / technical sparring partner - industrial HW-/SW product development at WAGO
I support the development of an industrial automation and communication product in which hardware, firmware, embedded software, system architecture, and testing work closely together. As a coach and technical sparring partner, I support product and project owners as well as development teams in turning product goals into a clear technical delivery structure.
- Technical Vision & Strategy: translated product goals into prioritized requirements, milestones, decisions, and executable work packages for HW-/SW teams.
- HW-/SW Collaboration: structured the interfaces between hardware, firmware, Embedded Linux/RTOS, fieldbus/connectivity, system test, and product management; made risks and dependencies transparent.
- Coaching & Leadership Sparring: clarified roles, responsibilities, prioritization, and decision paths with technical leads and teams and strengthened cross-disciplinary collaboration.
Methods & environment: Polarion, GitHub, requirements engineering, configuration management, PROFINET, embedded systems, agile delivery, coaching, and facilitation.
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.
Abhishek Sharma
Last position:
Business Process Manager / SAP FICO Owner at Dynapac GmbH
- Defined S/4HANA finance solution architecture and led full project lifecycle — Blueprint through Hypercare — for global rollout.
- Designed global finance templates and COPA characteristics, harmonizing financial reporting across business units.
- Implemented role-based authorizations, SOD controls, and master data governance; managed provisioning and training for 200+ users.
- Coordinated cross-module integrations (MM, SD, PP) and third-party systems (Salesforce, SAP DRC, E-Invoicing), reducing month-end close from 5 days to 2 days.
- Prepared functional specifications, supported ABAP development, and drove problem management practices that reduced recurring incidents.
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
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
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.
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.
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.
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.
Christian Frauer
Last position:
Department Head (Interim) at Municipal utility 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 for the group
- Creating an IT strategy
- Designing templates, guidelines, and processes for standardized work
- Capturing strategic guardrails and grouping ongoing projects – deriving a roadmap for strategic planning
- Reviewing ongoing projects
- Creating staffing and capacity calculations
- Defining job profiles
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.
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
67%
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 6 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology 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 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 6 Sep 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 checks accuracy, completeness, consistency, timeliness, and validity across source systems, warehouses, and dashboards. Strong work here reduces broken reports, bad decisions, and manual cleanup.
Typical work
- Rule design for valid values, ranges, and formats
- Duplicate detection and record matching
- Missing-data checks and anomaly flags
- Reconciliation between source and target systems
- Monitoring for pipeline drift and broken feeds
Tools and ecosystem
Work often spans SQL, Python, dbt, Great Expectations, Soda, Monte Carlo, and testing built into ETL or ELT pipelines. Professionals also use catalog, lineage, and observability tools to trace where bad data starts and how it spreads.
When to bring in help
Companies hire freelance specialists when reports no longer match, migrations introduce edge cases, or new sources enter the stack. They are also useful during warehouse redesigns, master data cleanup, and before analytics or AI work depends on trusted inputs.
What strong specialists do
Good specialists do more than run checks. They define business rules with stakeholders, write maintainable tests, document exceptions, and set up alerts that teams can act on. They also know how to balance strict controls with real operational needs.
How teams work with them
Data quality work fits both remote and on-site collaboration. Remote specialists can review schemas, query logic, and pipeline behavior from anywhere, while on-site time helps when rules depend on local processes, legacy systems, or close work with business users.
Frequently asked questions
Quick answers to the questions that come up most around Data Quality.
Data quality means data is trustworthy enough for the task at hand. In practice, that includes valid formats, no unwanted duplicates, stable definitions, and values that match the source of truth. Good work also catches issues early, before they reach dashboards, models, or customer-facing systems.
A strong Data Quality specialist fixes broken validation rules, inconsistent identifiers, missing fields, and bad joins between systems. They also clean up reference data, reconcile totals, and trace errors back to the pipeline step where they started. The goal is to stop the same defect from coming back.
Data Quality focuses on whether data is usable, while data governance defines ownership, policies, and standards. Data engineering moves and transforms data, but it does not automatically ensure it is correct. In real projects, the three work together, but quality needs its own rules, tests, and review process.
A capable data quality freelancer usually knows SQL and Python well, and can work with dbt, Great Expectations, Soda, or similar testing tools. They should also understand schemas, source-to-target mapping, basic statistics, and how to write rules that business teams can maintain. Clear documentation matters just as much as tooling.
Simple Data Quality tasks can be handled by a specialist who knows testing patterns and pipeline checks. Messy master data, multi-system reconciliation, or quality rules for regulated data need deeper domain knowledge and stronger modeling skills. The harder the data flows and ownership boundaries, the more experience matters.
Most Data Quality work can be done remotely because it depends on queries, logs, schemas, and pipeline behavior. On-site time helps when the rules live in legacy systems, when business users need workshops, or when data definitions are still being negotiated. Many teams use a mix of both.
Look for someone who starts with the business meaning of the data, not just the table structure. A strong Data Quality specialist can explain why a rule exists, show how it is tested, and describe how exceptions are handled. Good signs are clean documentation, maintainable checks, and a clear plan for alerting and ownership.
No. Data Quality includes cleansing, but it also covers validation, monitoring, root-cause analysis, and prevention. Cleansing without better checks only treats the symptom. The best specialists build controls that reduce future cleanup work.
The average hourly rate of freelancers who have used Data Quality in their recent projects is 99 €, which corresponds to a daily rate of about 792 € based on an 8-hour working day.
Of the freelancers who have used Data Quality in their recent projects, 96% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers 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 who have used Data Quality in their recent projects are German (98%), English (97%), and French (14%).
The most common industries among freelancers who have used Data Quality in their recent projects are Information Technology (74%), Professional Services (49%), and Banking and Finance (41%).
The most common business areas among freelancers who have used Data Quality in their recent projects are Information Technology (87%), Business Intelligence (69%), and Project Management (61%).
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
