
Data Quality Expert
to turn unreliable data into trusted decisions, matched in minutes with vetted professionalsHire experts who profile datasets, define validation rules, and improve data pipelines across CRM, ERP, analytics, and master data environments. Get precise access to vetted, available freelancers who fit your Data Quality requirements quickly.
Meet FRATCH Experts who have recently used Data Quality
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company’s BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across departments
- Forecasting & Planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enabling rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing and consistent modeling using Star Schema, DAX and Power Query
- Incident & Anomaly Analysis: Identification, investigation and explanation of data anomalies, including root-cause analysis and specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email delivery of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Felix B.
Last position:
Digital Consultant and Solution Manager at Vorwerk SE & Co. KG
Launched a new digital product in the Chinese market as part of a global growth strategy.
Served as the link and coordinating interface between central product management, local Chinese teams and international stakeholders.
Managed the overall program to coordinate complex digital projects.
Successfully launched a digital product in China (including successful certification for the Chinese market).
Balanced local requirements with global priorities while taking tax and regulatory requirements into account.
Introduced a new way of working to improve collaboration and coordination across locations.
Developed and implemented a scaling strategy for the digital business model in China.
Used Jira, Confluence, Miro, Scrum, Agile ways of working, SAFe.
Project language: English.
Michael H.
Last position:
Interim Head of IT / Transformation Manager at A German medium-sized industrial distributor, B2B e-commerce
Agile Transformation: introducing agile principles and transforming traditional IT departments into Scrum and Kanban teams
IT Operating Model: redesigning the IT organization and introducing modern delivery structures and roles
Flight Levels: building up Flight Levels practices within IT to align strategy, coordination and operational execution
Leadership & Coaching: recruiting Agile Coaches and coaching Scrum Masters and IT leaders
ERP Operations: acting as Interim Product Owner for the existing ERP landscape and ensuring business continuity
ERP Transformation: reorganizing and supporting the ERP migration as a Project Manager; Phase 1 (financial accounting) live since October 2025
Operational Core (ERP): establishing dedicated teams for ERP Support, Feature Development, and ERP Migration
Modern Workplace: initiating the transition towards a Microsoft 365-based cloud workplace environment
Integration: establishing a dedicated team to drive integration and connectivity initiatives
Warehouse Operations: building a cross-functional team to support logistics and warehouse-related capabilities
Maintenance & Operations (M&O): introducing a dedicated team for system engineering, security, stable operations and continuous improvement
AI Initiatives: identifying and promoting practical Artificial Intelligence use cases across the IT department
As Interim Head of IT for a German medium-sized industrial distributor, I am responsible for 31 IT employees and lead the digital and organizational transformation, reporting directly to executive leadership. I took over an IT department with almost no basic structures – no clear roles, processes, documentation or ticket system – and with significant technical debt that tied up most of its capacity. Introduced agile principles and ways of working in the IT organization and transformed the traditionally structured IT department into cross-functional Scrum and Kanban teams focused on transparency, collaboration, and customer value delivery.
Redesigned the entire IT organization by establishing a modern operating model, introducing new roles such as Product Owner, Scrum Master and Solution Architect and strengthening the organization through strategic hiring and targeted capability development. Recruited and onboarded Agile Coaches to accelerate the transformation and personally managed and coached Scrum Masters as well as IT department leaders to foster servant leadership, self-management and a culture of continuous improvement.
Aligned IT-internal planning across eight teams to three Flight Levels based on Klaus Leopold (strategy, coordination, team), laying the foundation for later company-wide portfolio management. Consolidated the backlog of more than 10 workstreams and prioritized it by business value and cost of delay. Introduced a ticketing system to increase transparency and enable data-driven decision-making, flow-based work management, and greater accountability at all levels.
Established and supported multiple cross-functional teams, including the Operational Core (ERP) domain with dedicated streams for Support, Feature Development and ERP Migration, as well as teams responsible for Warehouse Operations, Maintenance & Operations (M&O) including system engineering and security, Modern Workplace, Data Quality and integration initiatives. Worked closely with Agile Coaches to support team development, remove impediments, and continuously improve delivery effectiveness.
Acted as Interim Product Owner for the operation and enhancement of the existing ERP landscape while supporting the ERP migration as a Project Manager. Reorganized the project organization and had the already negotiated contract with the implementation partner realigned with a focus on approach and overall responsibility; Phase 1 (financial accounting) has been live since October 2025. Collaborated with the selected implementation partner to design and establish the project organization and target operating model for the ERP transformation, introducing agile delivery structures and ways of working. Facilitated the alignment between business, IT and the external vendor and played a key role in negotiating the contractual framework and delivery model to ensure a sustainable foundation for the transformation program.
Initiated the strategic modernization of the workplace environment through the adoption of Microsoft 365 cloud capabilities, establishing the foundation for enhanced collaboration, scalability and future digital services.
Launched AI initiatives to identify practical business use cases and promote the responsible adoption of Artificial Intelligence across the IT department. Encouraged self-management and empowered teams to take ownership of decisions, delivery and continuous improvement.
Franz B.
Last position:
Product Development (AI) at Self-initiated
AI phone assistant platform
Claude Code, Google AI Studio, Python, LLM / Voice-AI, PostgreSQL
- Designed and hands-on developed an AI-powered phone assistant platform (Voice-AI / LLM)—from idea and architecture through to MVP/product.
- Built agentic workflows and end-to-end automations with Claude Code and Google AI Studio.
- Also supported AI-powered delivery in client engagements: using Claude Code for governance documentation, requirements drafts, and automations.
Reinhold M.
Last position:
Business Requirements Engineer, Proxy Product Owner, Tester at AXA Konzern AG
Working Environment
- Insurance / Claims Management (Property, Liability, Automotive)
- REST-based ClaimAPI with Guidewire ClaimCenter
Scope
- Further development of the internal REST-based ClaimAPI with Guidewire ClaimCenter as the backend and various portals/front-end offices as clients, and with the following objectives: + increased AI-supported claims processing outside of Guidewire ClaimCenter + and support for all lines (property, liability, automotive).
- Further development of automated claims processing
Activities
- Interfacing IT & Business
- Design activities with documentation in Confluence, Jira: + Requirements engineering (functional and non-functional) + Design of REST endpoints and coordination with interface partners (esp. AI-colleagues) + Identification, design and evaluation of alternative solutions (moving away from a monolithic approach toward fine-grained endpoints, with code reuse across all lines if possible) + Creation / refinement of product backlog items in Jira, i.e. epics, user stories and tasks, incl. alignment with developers & POs during (pre-)refinement sessions + Using AXA Secure GPT when creating / refining user stories (description, acceptance criteria) and designing REST endpoints + Estimation and prioritization of user stories and tasks during Sprint Planning sessions + Coordination and support during the implementation of Product Backlog items + Driving decision making with preparation and presentation of decision-making documents
- Quality assurance: + Test management, including test case creation using Xray in Jira + Test execution using Bruno as REST client and in Guidewire ClaimCenter + Submission of test artifacts for PO approval + Analysis of production bugs
Methods Big Room Planning (aka PI-Planning in SAFe), Scrum, BPMN, EPK, UML, ERM, DDD
Tools / Tech Stack Teams, Confluence, Jira with Xray, OpenAPI/Swagger, REST, JSON, XML, MS Office, Bruno, GitHub repository (reading), Guidewire ClaimCenter, AXA Secure GPT (AXA's in-house GenAI solution based on Azure OpenAI)
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
Jens L.
Last position:
Consulting Digital Development & Transformation at European Mar GmbH
Developed the product vision and outcome strategy for a new registry platform, designed a management workshop for strategic alignment, conducted BPMN process mapping across systems and roles, analyzed user story practices and further developed them on an outcome basis, and brought together stakeholders from business departments, management and the external development partner.
Skills: Product Strategy, Outcome Roadmapping, Stakeholder Workshops, BPMN/Process Analysis, Miro, Linear
Alexander F.
Last position:
Freelance Business Consultant and Interim Manager at A. Folch von Sydow INTERIM MANAGEMENT
Advising various companies in the areas of controlling, finance and planning, transformation, business partnering, management reporting and AI-driven forecasting systems
Christian P.
Last position:
Power BI Developer & Consultant in an Agile Team (SAFe) at DB InfraGO AG
Further Development of the Integrated Capacity Management Platform InKa
Rail Infrastructure
- Power BI shared datasets and reports via DirectQuery on Amazon Redshift (timetable, construction and capacity data)
- Automated report testing with Playwright in Docker containers, version control with GitLab
- Concept and implementation of user communication (information page, changelog, known bugs)
Initial Situation:
The integrated capacity management platform (InKa) is a monitoring and analysis platform for data from timetable and capacity management: customer orders, infrastructure, timetable operations, construction, as well as route capacity and width. The data is stored and linked in a structured data warehouse. This makes it possible to create complex KPIs across different source systems and display current and future route utilization as well as construction activity across the network. The goal is to improve strategic, medium-term and intra-year planning and provide users with easy access to capacity management analyses.
Measures:
- Enablement: Introduction of Power BI with training and workshops for development teams.
- Consulting & Design: Problem analysis, concepts and solution consulting for reporting requirements; implementation of reporting solution plans.
- Data Model: Development of shared datasets with DirectQuery on Amazon Redshift and import tables; DAX measures for utilization and capacity KPIs.
- Development: Data analysis, data transformation and visualization in Power BI; extension and migration of business applications to Power BI; AI-supported development in VS Code with GitHub Copilot.
- Test Automation: Automated testing of Power BI reports with Playwright; setup of the test environment in Docker containers.
- User Communication: Concept and implementation of a central information page with hot news, changelog and known bugs, access from reports with report filters, updates via Power Automate.
- Deployment: Management of Power BI Deployment Pipelines across the DEV, TEST and PROD environments.
- Agile Working: Work in an agile team according to SAFe (Scaled Agile Framework); refinement of user stories and features including effort estimates in Jira; version control with Git and GitLab.
- Quality & Documentation: Reviews for quality assurance, error analysis and resolution, technical documentation in AsciiDoc and Confluence.
Technologies: Power BI, DAX, Power Query, DirectQuery, Amazon Redshift, Git / GitLab, Deployment Pipelines, Playwright, Docker, AsciiDoc, Power Automate, Jira, Confluence, VS Code with GitHub Copilot, SAFe
Results:
- Capacity management KPIs from multiple source systems combined on one platform
- Transparency regarding current and future route utilization and construction activity across the network
- Easy access to analyses for users
- Automated regression testing of Power BI reports
- User communication implemented: central information page with hot news, changelog and known bugs, directly accessible from the reports
Oliver V.
Last position:
Interim Manager and Management Consultant at Self-employed
- Developed a market entry strategy for the claims division of an insurance services provider.
- Analyzed and optimized existing claims processes.
- Defined management metrics and KPIs to improve performance and efficiency.
- Advised management on market positioning and process digitalization.
- Led an IT team of 13 employees as part of an interim vacancy cover.
- Ensured stable IT operations and managed external IT service providers.
- Led regulatory projects, particularly the implementation of the DORA regulation.
- Prepared and supported an IT security audit.
- Change management and conflict moderation in a challenging transformation environment (FI migration).
Amit S.
Last position:
IT Project Manager – SAP S/4 HANA Delivery Consultant at CHG Meridian UK Limited
- Managed the integrated SAP S/4HANA delivery plan for 12 BTS workstreams, including milestone, dependency, and governance management.
- Coordinated end-to-end delivery across Business, IT, and Operations, focusing on execution, readiness, and issue resolution.
- Responsible for RAID, test, cutover, and deployment management to ensure a successful go-live.
- Implemented KPI tracking, status reporting, and PMO governance to provide transparent management of performance, risks, and escalations.
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and maintained the product backlog
Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural
Fred K.
Last position:
Project Manager Digital Restructuring at Northrop Grumman LITEF GmbH
Assignment focus: Leading digital restructuring in a highly regulated Defence and Aerospace environment. Focus areas: improving data flows and data quality, introducing paperless data collection in production, integrating machine data for automatic SPC calculation and process control, as well as cybersecurity analyses.
Main objectives:
- Analyze, structure and improve data flows and data quality in production
- Introduce paperless data collection in manufacturing areas
- Integrate machine data for automatic SPC data calculation and process control
- Implement digital restructuring while meeting regulatory requirements in the Defence environment
Measures and results:
- Analyzed and structured existing production data flows as a basis for control and reporting
- Established mechanisms to ensure data quality and availability across manufacturing areas
- Introduced paperless data collection in production — replacing manual and paper-based processes
- Integrated machine data for automatic SPC data calculation (control charts, Cp/Cpk) and real-time process control
- Conducted cybersecurity analyses in the production and IT environments
Area of responsibility: Project management for digital restructuring, paperless production, data management and data quality in a highly regulated Defence and Aerospace environment.
Elisabeth W.
Last position:
Executive Advisor | Business Potential & Strategy Implementation & Managing Partner at Wesse Consult
European business consultancy helping consumer goods and FMCG companies unlock untapped potential, define winning strategies, and execute with precision and measurable impact.
- Analyzing business challenges with ~20% effort to drive ~80% output, ensuring clear priorities, structures and actionable recommendations that deliver rapid, measurable results.
- Driving holistic Operations and business transformation, cost efficiency, customer focus, go-to-market strategy and KPI-driven performance.
- Acting as a trusted C-Level advisor and sparring partner to turn strategy into measurable results; helping CEOs, founders and leadership teams in businesses of all sizes to strengthen growth and profitability.
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.
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
3.1 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
95%
Master's degree or higher
67%
Doctorate
10%

Certifications per freelancer
3

Most common languages
German, English, Spanish

Speak two or more languages
98%
Based on our profile pool as of 5 Nov 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 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 5 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Quality 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 (75%)
- Professional Services (51%)
- Banking and Finance (42%)
- Manufacturing (38%)
- Retail (34%)
- Automotive (33%)
- Energy (24%)
- Healthcare (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Quality Covers
Data Quality is the discipline of measuring, improving, and maintaining the fitness of data for business use. Specialists examine accuracy, completeness, consistency, timeliness, validity, and uniqueness across operational and analytical systems. The work turns scattered records into data people can trust.
Typical Use Cases
Companies bring in Data Quality expertise when critical information is duplicated, incomplete, outdated, or contradictory between systems.
- Profile customer, product, supplier, and transaction data
- Detect duplicates, missing values, and invalid formats
- Standardize names, addresses, codes, and reference data
- Monitor quality rules in reporting and data platforms
- Prepare datasets for migration, analytics, or machine learning
Ecosystem And Tools
Data Quality work connects source applications with data warehouses, lakes, integration layers, and governance programs. Professionals may use SQL, Python, spreadsheets, ETL and ELT tools, data catalogs, observability products, and master data management systems. They also work with validation frameworks and automated pipeline checks.
When To Hire Specialists
Freelance specialists are useful during system migrations, mergers, CRM cleanups, regulatory data reviews, and analytics launches. They can establish a baseline, prioritize remediation, and transfer repeatable controls to internal teams. In Germany and other markets, remote collaboration is common, while workshops may still benefit from on-site or local-language support.
What Strong Professionals Deliver
A strong professional combines investigation with practical remediation. They trace defects to their source instead of correcting symptoms in reports, document business definitions, and make quality rules testable. Clear ownership, measurable checks, and useful exception workflows matter as much as the cleanup itself.
- Data profiling with clear findings and priorities
- Reusable validation and monitoring rules
- Safe cleansing with documented transformations
- Quality dashboards that support accountable decisions
Skills To Evaluate
Look for experience with the systems that hold your most important data and with the outcomes your project requires. Relevant adjacent skills include data governance, master data management, metadata management, SQL, pipeline design, privacy controls, and business analysis. Ask candidates to explain how they protect source data, validate changes, and prove that quality has improved.
Frequently asked questions
Quick answers to the questions that come up most around Data Quality.
Data Quality is used to make information accurate, complete, consistent, valid, unique, and timely enough for a defined business purpose. Companies apply it to customer records, finance data, product catalogs, operational systems, reporting, and machine learning inputs.
Data Quality is a broader management discipline than data cleansing. Cleansing corrects or removes problematic records, while Data Quality also covers profiling, rules, monitoring, ownership, prevention, and ongoing measurement.
Data Quality work often overlaps with data governance, master data management, metadata management, SQL, ETL or ELT design, and data observability. Strong specialists also understand the business definitions behind fields and can work with system owners to resolve root causes.
Data Quality projects vary with the number of source systems, the condition of the records, and the impact of errors. A focused profiling or cleanup assignment may need a narrower skill set, while enterprise governance and monitoring require experience across architecture, processes, and stakeholder groups.
Data Quality work is often suitable for remote collaboration because profiling, rule design, documentation, and dashboard reviews happen in shared systems. On-site workshops can help when specialists must map processes with business teams or access restricted environments, and local language skills may matter for field definitions and stakeholder interviews.
Data Quality expertise is best assessed through a concrete example of profiling, root-cause analysis, remediation, and monitoring. Ask how the specialist defines acceptable quality, avoids damaging valid records, documents transformations, and demonstrates that controls continue to work.
Data Quality deliverables commonly include a profile of key datasets, a defect and impact analysis, business rules, cleansing logic, exception handling, and monitoring documentation. The specialist should also explain ownership and provide practical handover materials for ongoing control.
Data Quality requires more than validation when errors originate in upstream processes, unclear definitions, or duplicated ownership. Validation can flag a problem, but lasting improvement may require changes to source applications, master data processes, governance decisions, and pipeline design.
The average hourly rate of freelancers who have used Data Quality in their recent projects is 98 €, which corresponds to a daily rate of about 784 € based on an 8-hour working day.
Of the freelancers who have used Data Quality in their recent projects, 95% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 10% 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 3.1 years.
The most common languages among freelancers who have used Data Quality in their recent projects are German (98%), English (97%), and Spanish (15%).
The most common industries among freelancers who have used Data Quality in their recent projects are Information Technology (75%), Professional Services (51%), and Banking and Finance (42%).
The most common business areas among freelancers who have used Data Quality in their recent projects are Information Technology (88%), Business Intelligence (66%), and Project Management (60%).
Main locations of FRATCH Experts, who have recently used Data Quality
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Countries:
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