Data Quality Experts in Austria
matched in minutes with vetted, available freelance specialistsHire experts who profile datasets, define validation rules and improve data pipelines across CRM, ERP and analytics environments. Get fast, precise matching with vetted, available freelancers who fit your project needs.
Meet FRATCH Experts in Austria, who have recently used Data Quality
Alexander P.
Last position:
Owner & Lecturer at Own company for AI governance and data products, Vienna
- Consulting and interim management at the interface between IT operations and regulation
- Impact analysis and implementation planning for NISG 2026 and the EU AI Act, including risk management and reporting and evidence processes
- Training for governing bodies and employees on regulatory obligations
- Lectures in Data & Information Management and Human-Machine Interaction at University of Applied Sciences Burgenland, since 2023
- Supervision of master’s theses and participation in the examination board
- Presentations for business and educational institutions
- Design and development of data and AI products, platforms and pipelines
- Privacy-first architectures and zero-knowledge encryption, cloud-native on EU infrastructure
- MLOps and AIOps in live operations
- Own applications under own brand: shared codebase, separate delivery for each target device
- AI-assisted software development (vibe coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Publications on data sovereignty, cloud economics and industrial image processing
- AI governance / compliance: data quality, Responsible AI, EU AI Act readiness, risk classification, AI ethics
Stefania D.
Last position:
Data Engineer at Storebox
Tech: AWS (Glue, Lambda, Redshift), Airflow, PostgreSQL, Python, PySpark, Metabase, Power BI
Delivered: Analytics-Ready Data Models • Legacy SQL to Cloud ETL Migration • Dynamic Pricing Engine
- Owned and evolved the company data warehouse end-to-end — from ingestion to transformation to analytics-ready dimensional data models on AWS Redshift.
- Collaborated with Analysts, Data Scientists, and business stakeholders to deliver scalable dimensional data models that enable self-serve analytics and streamline dashboarding in Metabase and Power BI.
- Architected end-to-end ETL/ELT pipelines on AWS (Glue, Lambda, Redshift) using Python and PySpark, orchestrated with Apache Airflow (MWAA) for reliability and observability.
- Defined and enforced data quality standards and governance practices across pipelines and the core data layer.
- Led migration of legacy SQL infrastructure into scalable AWS Glue pipelines with distributed PySpark processing, eliminating bottlenecks and reducing downtime.
- Developed a dynamic pricing engine applying automated promotional discounts based on occupancy rates, competitor pricing, and location performance tiers.
- Designed schema mappings to ingest MongoDB data into structured relational systems (Redshift/PostgreSQL).
Elija L.
Last position:
Fujitsu Germany
- Supporting and helping shape the setup and further development of a company-wide HUB system in the Fujitsu environment for integrating a wide range of data sources and applications in an international context
- Designing and implementing data modeling, ETL processes, reporting, and data quality management
Franz P.
Last position:
Business & IT Consulting at fpcon Franz Plakolm Consulting
- Business consulting finance & supply chain for Infor ERP LN Cloudsuite projects
- Lead consultant ERP LN Cloudsuite finance & cost accounting
- Implementation projects in Germany for a manufacturer of precision castings and a producer of stainless steel kegs
- Optimization project at a steel service center
Shahram F.
Last position:
Senior Product Owner at Elderly Neighbour Watch
Delivered a digital, non-profit neighborhood platform to connect older adults with volunteers for practical support and social companionship. Responsible for business analysis, requirement definition, platform evaluation and delivery of a locally scalable service solution focusing on AI-driven enhancements, data-based optimization and clear market positioning. Evaluated several low-code/business platforms including Odoo and Glide Apps and assessed monday.com as a CRM-like option before selecting Glide Apps for implementation. Implemented a non-native mobile and desktop application with Glide Apps and the web presence with WordPress. Tasks
- Gathering, analyzing and structuring business requirements for the product and service model
- Conducting market, target group and competitor analyses to position the offering
- Evaluating and selecting suitable low-code/business platforms for implementation
- Defining core user journeys, business processes and operational workflows for older adults, volunteers and administration
- Translating business requirements into functional requirements for profiles, task management, coordination, communication and notifications
- Guiding the implementation of the non-native mobile and desktop application in Glide Apps as well as the web presence in WordPress
- Preparing the business side for future AI-powered features such as intelligent matching and data-based optimization
- Ensuring compliance with GDPR requirements Results
- Successfully delivered digital support platform connecting older adults with volunteers
- Solid foundation for market positioning through market, target group and competitor analyses
- Delivered a functional non-native mobile and desktop application with Glide Apps and a supporting web presence with WordPress
- Established foundation for scaling and AI-based further development
Christian P.
Last position:
Migration Lead for Accounting & Year-End Closing Tests at Raiffeisenverband Salzburg
- Project and test management for the accounting migration
- Coordination of resources
- Planning, creation and alignment of scenarios with functional experts and IT product owners
- Responsibility for accounting KPIs of the migration cockpit
- System environment: Jira, XRay, Confluence
Michael G.
Last position:
Scrummaster at Xenovo
Planning and monitoring sprints and holding the scheduled meetings
Analyzing and resolving issues in the development process
Error analysis, bug management and correction in individual process steps
Stakeholder management
Documentation and improvement of the development process
Tools: JIRA, Confluence
Zeev T.
Last position:
Enterprise Architect at Coveris Group
- Built Enterprise Architecture practice covering business, application, data, and integration layers
- Created a multi-year Integration Strategy, delivering annual cost savings of several hundred thousand euros
- Defined portfolio simplification roadmap, identifying redundant systems and cost reduction opportunities
- Designed target-state enterprise architecture around D365 FSCM, enabling a best-of-breed future landscape
- Developed Master Data Management (MDM) strategy and roadmap to improve data quality and operational efficiency
- Provided enterprise-wide architectural direction ensuring alignment and eliminating redundant initiatives
- Established reusable architecture patterns adopted by delivery teams
- Facilitated cross-functional architecture forums to align product and delivery teams
- Translated enterprise architecture guidance into practical delivery decisions
- Drove practical adoption of architecture guidance across initiatives rather than acting as a gatekeeper
- Identified redundant systems enabling substantial cost reduction potential
- Enabled cost transparency in integration landscape
- Enabled fact-based investment prioritization decisions
Gerald G.
Last position:
Data Design Authority at Department of Government Enablement
- Responsible for realigning the data architecture of the Abu Dhabi government to achieve a fully AI-driven public administration
- Definition of modeling standards
- Creation of a conceptual and logical model for the entire Abu Dhabi government administration
- Definition of data quality and data security standards
Alexander T.
Last position:
Functional Analyst & Administrator at Netz Niederösterreich GmbH
- Introduced automated testing for Fabasoft components and trained testers to create automated test cases.
- Installed AppTest on the server and clients.
- Requested firewall exceptions to allow communication between the AppTest server and clients.
- Conducted training sessions for testers.
- Automated test workflows.
- Created reports and sent emails.
- Enabled faster reviews of new releases, reduced manual testing effort, and improved documentation of test results and discovered defects.
Johanna U.
Last position:
Manager at Deloitte Consulting GmbH
- Technology consulting and project management focused on SAP ICMR and SAP Group Reporting
Qamile B.
Last position:
SAP BI / SAP Analytics Cloud at ARDDEX Group
- Gathering requirements and conceptually designing reporting and planning requirements in controlling (CO) and modeling SAP Analytics Cloud data models for group reporting.
- Designing and implementing reports and dashboards to support planning, forecasting and control processes.
Discover over 15,000 top freelancers
Statistics of experts using Data Quality
Aggregated from the professional profiles of matched freelancers.
Experience
23 years
Position duration
3 years
Positions per freelancer
14
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Banking and Finance, Information Technology, Professional Services
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
78%
Master's degree or higher
56%
Doctorate
11%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Austria 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 Austria 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 19 Sep 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.
- Banking and Finance (83%)
- Information Technology (83%)
- Professional Services (50%)
- Government and Administration (50%)
- Automotive (33%)
- Education (33%)
- Manufacturing (33%)
- Retail (33%)
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 making business data accurate, complete, consistent, timely, valid and fit for its intended use. It combines profiling, validation, cleansing, enrichment and monitoring across structured and unstructured sources. The work supports trustworthy reporting, automation, customer records and operational decisions.
Typical project outcomes
- Profile customer, product, supplier and financial datasets
- Detect duplicates, missing values, invalid formats and conflicting records
- Standardize names, addresses, codes and reference data
- Define quality rules for analytics, migration and integration
- Create dashboards and alerts for ongoing data health
Strong quality work turns unreliable source data into governed, usable information. It can support a warehouse migration, a master data program, a regulatory reporting process or a new machine learning initiative.
Tools and adjacent skills
Experts may work with SQL, Python, dbt, Great Expectations, Soda, Monte Carlo, Informatica, Talend, Microsoft Purview or cloud-native data services. The right tooling depends on the data landscape and the team’s operating model. Useful adjacent skills include data governance, metadata management, data lineage, master data management and ETL or ELT design.
When companies need specialists
- Reports disagree across departments or source systems
- A CRM, ERP or warehouse migration exposes inconsistent records
- New pipelines lack controls for freshness, completeness or validity
- Teams need ownership, issue workflows and measurable quality rules
Freelance expertise helps when internal teams know the business context but lack capacity for a focused assessment or remediation effort. In Austria, specialists may also support collaboration between local business teams, international data owners and remote delivery partners.
What strong professionals deliver
A strong specialist connects technical checks with business definitions. They document critical data elements, trace problems to their source, prioritize remediation and make rules visible to the teams responsible for each dataset. They also distinguish a one-time cleanup from controls that prevent the same issue from returning.
Clear deliverables can include a profiling report, quality scorecards, rule catalogs, exception queues, lineage documentation, cleansing logic and operating procedures. Good professionals explain trade-offs instead of hiding uncertainty behind a single score.
How quality becomes sustainable
Data Quality is not finished when a dataset has been cleaned once. Sustainable results require ownership, validation in pipelines, monitoring after release and a process for resolving exceptions. Governance should be practical enough for teams to follow and precise enough to support audits and reliable analysis.
The best specialists leave behind reusable checks, clear documentation and a handover that internal teams can operate. They measure improvements against agreed business definitions and revisit rules when products, processes or source systems change.
Frequently asked questions
Before you brief your next project: the most common questions about Data Quality.
Data Quality is used to make information accurate, complete, consistent, valid and timely enough for business use. Companies apply it to customer records, product data, financial reporting, analytics, migrations, integrations and machine learning inputs.
Data Quality focuses on the condition and usability of data, while data cleansing corrects specific errors and data governance defines ownership, policies and decision rights. They work together: governance sets the operating model, cleansing addresses defects and quality controls help prevent them from returning.
Data Quality work often overlaps with SQL, Python, ETL or ELT, data warehousing, master data management and metadata management. Experience with data lineage, profiling tools, cloud platforms and business process analysis is also valuable.
Data Quality projects vary with the number of sources, criticality of the data and maturity of existing controls. A focused profiling exercise may need a narrow specialist skill set, while enterprise remediation requires experience with governance, stakeholder alignment, pipeline controls and change management.
Data Quality work is often suitable for remote collaboration because profiling, rule design, documentation and monitoring can be performed securely online. On-site workshops may still help when specialists need to map local processes, clarify ownership or align Austrian business teams with international data owners.
Data Quality tools should be compared against the systems, data volumes and workflows they must support, not only by feature lists. Assess rule flexibility, integration with existing pipelines, lineage, alerting, issue management, access controls and the effort required for teams to maintain checks.
Data Quality deliverables can include a profiling assessment, critical data element inventory, validation rules, cleansing logic, exception workflows, dashboards and documentation. A well-scoped engagement also explains ownership, priorities and how controls will run after the specialist leaves.
Data Quality expertise is visible in clear business definitions, traceable findings and controls that operate in real pipelines. Ask how the specialist handles false positives, prioritizes defects, documents assumptions and proves that improvements remain stable after remediation.
The average hourly rate of freelancers in Austria who have used Data Quality in their recent projects is 117 €, which corresponds to a daily rate of about 938 € based on an 8-hour working day.
Of the freelancers in Austria who have used Data Quality in their recent projects, 78% hold at least a Bachelor's degree, 56% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Austria who have used Data Quality in their recent projects have 23 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Austria who have used Data Quality in their recent projects are German (100%), English (100%), and French (42%).
The most common industries among freelancers in Austria who have used Data Quality in their recent projects are Banking and Finance (83%), Information Technology (83%), and Professional Services (50%).
The most common business areas among freelancers in Austria who have used Data Quality in their recent projects are Information Technology (100%), Business Intelligence (92%), and Product Development (75%).
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.
Vienna Graz 
Vienna Countries:
- Germany
- Austria
- Switzerland
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!
Philipp Thomaschewski
FRATCH CEO
