Data Quality Experts in Vienna
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Meet FRATCH Experts in Vienna, who have recently used Data Quality
Shahram Fallahdoust
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
Michael Gonschor
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 Turchinsky
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
Lorenz Graiff
Last position:
Business Analyst at Wirtschaftsagentur Wien
- Conducting a feasibility study on introducing an in-house DWH as a central data source
- Conducting workshops for current state analysis (processes, reports, KPIs) together with the clients
- Gathering and detailing business requirements including a target concept for an in-house DWH
- Coordinating and clarifying data deliveries and interfaces in meetings with stakeholders and data providers
- Developing initial data models as a basis for data quality and later implementation
- Drafting solution variants including architecture and operation options and decision basis
- Creating the business case including effort estimates, cost-benefit analysis, and decision report
Gerald Gastgeb
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
Johanna Urbanz
Last position:
Manager at Deloitte Consulting GmbH
- Technology consulting and project management focused on SAP ICMR and SAP Group Reporting
Qamile Bufaj
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
24 years
Position duration
1.6 years
Positions per freelancer
17
Top business areas
Business Intelligence, Information Technology, 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
80%
Master's degree or higher
40%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
86%
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 Vienna 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 Vienna 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
Data you can trust
Data quality keeps records accurate, complete, consistent, and fit for use. It matters in reporting, customer data, product catalogs, finance, and operations. Strong specialists turn messy source data into dependable data that teams can use without constant manual checks.
What they deliver
- Profiling and rule design for missing, duplicate, and invalid values
- Validation checks for pipelines, warehouses, and integration flows
- Cleansing logic for master data and reference data
- Monitoring and alerts for recurring data issues
Core methods
Data quality work often includes data profiling, standardization, deduplication, and exception handling. Professionals also define thresholds, ownership, and review steps so problems are caught early and fixed at the source. In many teams, this is closely tied to data governance and master data management.
Common tools
The tool stack depends on the environment. Specialists may work with SQL, Python, dbt, Great Expectations, Informatica, Talend, Collibra, or built-in checks in cloud data platforms. Strong professionals know how to fit these tools into the existing warehouse, lake, or ETL setup.
When to bring in help
Companies usually bring in freelance expertise when reports do not match, source systems drift, or a new platform needs clean migration rules. In Vienna, this is common for organizations with shared service, finance, logistics, public sector, or regulated data flows that need careful controls and clear documentation.
What strong specialists do
Strong specialists look beyond broken rows. They trace issues back to source systems, define practical rules, and write checks that teams can maintain. Good work also includes clear ownership, simple documentation, and a setup that business and technical teams can both understand.
Frequently asked questions
Quick answers to the questions that come up most around Data Quality.
Data Quality work covers the rules and checks that keep data usable across systems and reports. It includes profiling, validation, cleansing, deduplication, and ongoing monitoring. The goal is not just clean tables, but data that teams can trust for daily decisions.
Data Quality focuses on the condition of the data itself and the checks that keep it reliable. Data governance defines ownership, policy, and control, while master data management focuses on shared reference records across systems. In practice, the three areas often work together.
A strong Data Quality freelancer helps when reports conflict, migrations expose hidden issues, or a new pipeline needs validation from day one. Companies also bring in specialists when they need a short, focused review of rules, exceptions, and root causes. That is often faster than asking internal teams to guess their way through the problem.
A good Data Quality specialist usually knows SQL, data profiling, scripting, and basic data engineering patterns. Familiarity with ETL, warehouses, master data, and governance tools is also common. The best professionals can talk to both technical teams and business owners without losing clarity.
A Data Quality specialist uses tools like Great Expectations to turn business rules into repeatable checks. They define expectations, wire them into pipelines, and decide what should fail, warn, or pass with review. The tool matters less than the rule design and the ability to keep checks maintainable.
Most Data Quality work can be done remotely if access to source systems, sample data, and stakeholders is set up well. On-site time in Vienna can help when teams need workshop-style rule definition or when sensitive systems require tighter coordination. Many projects use a mix of both.
Look for a Data Quality professional who asks about sources, ownership, and failure handling before suggesting tools. Good signs are clear rule definitions, practical documentation, and examples of how they handled edge cases or bad upstream data. They should improve the process, not just add checks.
Yes, Data Quality is relevant anywhere data moves and gets used. It matters in analytics, operational workflows, migrations, and warehouse reporting because weak source data quickly spreads downstream. The best specialists adapt their checks to the system, not the other way around.
The average hourly rate of freelancers in Vienna, Austria who have used Data Quality in their recent projects is 106 €, which corresponds to a daily rate of about 850 € based on an 8-hour working day.
Of the freelancers in Vienna, Austria who have used Data Quality in their recent projects, 80% hold at least a Bachelor's degree and 40% hold at least a Master's degree.
On average, freelancers in Vienna, Austria who have used Data Quality in their recent projects have 24 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Vienna, Austria who have used Data Quality in their recent projects are German (100%), English (86%), and French (29%).
The most common industries among freelancers in Vienna, Austria who have used Data Quality in their recent projects are Banking and Finance (100%), Information Technology (100%), and Professional Services (71%).
The most common business areas among freelancers in Vienna, Austria who have used Data Quality in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (86%).
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.
Countries:
Vienna 
Graz Request a free demo
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Philipp Thomaschewski
FRATCH CEO
