Data Quality Experts in Vienna
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Meet FRATCH Experts in Vienna, 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).
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
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
Lorenz G.
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 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
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
22 years
Position duration
1.9 years
Positions per freelancer
14
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, Human Resources
Bachelor's degree or higher
86%
Master's degree or higher
57%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
89%
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 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 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 (100%)
- Information Technology (89%)
- Professional Services (56%)
- Government and Administration (56%)
- Education (33%)
- Healthcare (33%)
- Manufacturing (33%)
- Real Estate (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 data fit for its intended use. It checks whether information is accurate, complete, consistent, timely, unique and valid across systems. Experts combine profiling, data cleansing and data validation to turn unreliable records into dependable business information.
Where it is used
Companies apply Data Quality practices wherever decisions, operations or customer experiences depend on reliable information. Typical work includes:
- Customer and supplier master data
- Reporting, analytics and business intelligence
- Data migrations and system consolidations
- Regulatory, finance and operational records
- Product, inventory and reference data
Methods and tooling
A quality initiative usually starts with profiling source data and defining measurable rules with business owners. Specialists may use SQL, Python, data catalogues, ETL tools and observability solutions to detect anomalies, duplicates and missing values. They also establish workflows for remediation, ownership and ongoing monitoring.
When companies need specialists
Freelance expertise is valuable during mergers, CRM or ERP changes, cloud migrations and data warehouse projects. It also helps when dashboards conflict, teams lack shared definitions or recurring errors reach customers. In Vienna, specialists may support local teams on site while coordinating securely with remote stakeholders across Europe.
Connected disciplines
Strong Data Quality work connects technical controls with business meaning. Useful adjacent knowledge includes master data management, data governance, metadata management, database design, API integration and data privacy practices. Experience with platforms such as Salesforce, SAP, Snowflake or Microsoft Azure can help when quality problems span several systems.
What distinguishes strong experts
The best professionals trace an issue to its source rather than only correcting visible symptoms. They translate business rules into clear checks, document assumptions and show how exceptions are handled. Look for practical evidence of profiling, rule design, deduplication, migration validation and monitoring, plus the communication skills to align data owners and technical teams.
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 and fit for business use. Companies rely on it for customer operations, reporting, analytics, migrations, compliance processes and automated decisions.
Data Quality is the broader discipline covering measurement, rules, prevention, monitoring and remediation. Data cleansing or data cleaning is one activity within it, focused on correcting, standardising or removing problematic records.
A strong Data Quality specialist often understands SQL, data profiling, ETL, master data management and data governance. Knowledge of metadata, APIs, cloud warehouses and privacy controls is useful when issues cross multiple systems.
The right level of Data Quality experience depends on scope, source complexity and the cost of errors. A focused validation or deduplication task may need a specialist with targeted delivery experience, while a cross-system programme calls for expertise in governance, migration and stakeholder alignment.
Data Quality work is often suitable for remote collaboration because profiling, rule design and documentation use shared environments. On-site workshops in Vienna can still be valuable for agreeing definitions, ownership and remediation processes with business teams.
A Data Quality expert defines checks before changing the data, such as validity, completeness, uniqueness and consistency rules. They compare results over time, trace exceptions to source systems and document which teams own corrective action.
A company may need a Data Quality freelancer before a CRM, ERP or warehouse migration, after repeated reporting discrepancies or when duplicate records affect operations. External expertise can provide an independent assessment and a practical remediation plan without adding permanent capacity.
Review whether the Data Quality professional can connect technical findings to business impact. Ask for examples of profiling, rule definition, deduplication, source correction and monitoring, and check whether their documentation makes ownership and repeatable controls clear.
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 848 € based on an 8-hour working day.
Of the freelancers in Vienna, Austria who have used Data Quality in their recent projects, 86% hold at least a Bachelor's degree and 57% hold at least a Master's degree.
On average, freelancers in Vienna, Austria who have used Data Quality in their recent projects have 22 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Vienna, Austria who have used Data Quality in their recent projects are German (100%), English (89%), and French (44%).
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 (89%), and Professional Services (56%).
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 (89%).
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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