Data Quality Experts in Vienna
for reliable data with precise AI matchingHire experts who assess data accuracy, design quality rules, and improve validation pipelines across CRM, ERP, analytics, and master data environments. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Vienna, who have recently used Data Quality
Alexander P.
Last position:
Owner & Lecturer at Artellico / Own company for AI governance and data products
- Consulting and interim management at the intersection of IT operations and regulation
- Impact assessment 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
- Teaching assignments in Data & Information Management and Human-Machine Interaction at Hochschule Burgenland, since 2023
- Supervision of Master's theses and participation on examination boards
- Industry talks for businesses 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
- Integration with bank account systems using SEPA direct debit mandates and message formats under ISO 20022 (pain.008, camt.053)
- MLOps and AIOps in live operations
- Own applications under my own brand: shared codebase, separate deployment for each target device
- AI-assisted software development (vibe-coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Professional publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Professional 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
Thomas P.
Last position:
Change Manager / AI-driven Finance Transformation at Agrana
Organizational Change Management for the strategic setup of a hybrid GBS rollout as part of a global, AI-driven Finance Transformation. End-to-end management of the OCM architecture in a highly complex on-, near-, and offshore partner setup [IN, PL, RO]. Successful completion of Phase 1 (Initial Strategy) and strategic pausing by the Executive Board after the early disclosure of critical vendor-side OPEX risks
Systemic Baseline Assessment & Stakeholder Diagnostics: Evaluation of the global organization and analysis of the process landscape across 21 countries [AT, DE, CH, FR, UK, SI, RO, UA, RU, US, CD, CN, AR, MX, ZA, IN, JP] to precisely determine change requirements
Architecting the OCM Strategy & Master Roadmap: Independent development of a comprehensive, AI-centered Change Roadmap for end-to-end management of all change initiatives across every project phase
Data Quality & AI Agent Risk Mitigation: Auditing of Process Data Repositories (Celonis analysis); prevention of the ingestion of 58% synthetic training data to safeguard AI model reliability amid geopolitical supply chain tensions
GBS Vendor BAFO Triage & Contractual Commitment: Critical review of Tier-1 vendor pitches and contractual commitment to a reduced Non-STP rate (from 7% to 4%) to avoid extreme manual rework with a planned FTE reduction (-63%)
Executive Enablement & Board Alignment: Design of the GBS Transformation Executive Board Deck to strategically enable C-level executives and transparently present completely overlooked AI tokenization costs
Targeted Change Communications & Engagement: Design and development of audience-specific communication measures to align the highly diverse, globally distributed stakeholder network
Adoption Velocity & Resistance Measurement: Engineering of quantitative measurement tools (Pulse Check Survey) using Behavioral Dyads to identify resistance based on data and ensure sustainable success measurement
Results:
- Avoidance of disruptive misallocations: Stopping the ingestion of 58% synthetic AI-agent training data after discovering only 42% compatibility with existing processes, preventing critical GBS cascade errors
- Significant OPEX & effort reduction: Reduction of the Non-STP rate in Tier-1 vendor BAFOs from 7% to 4% alongside an impending 63% Finance-FTE reduction; avoidance of thousands of time-consuming manual correction processes
- 100% Delivery of the OCM architecture & ROI protection: Delivery of all OCM artifacts (Change Roadmap, Comms Plan, Executive Board Deck, Pulse Check Survey) and protection of ROI by identifying incalculable LLM runtime token costs
Octavian G.
Last position:
Oracle DWH Analyst at Infomotion Gmbh
Oracle PL/SQL, Oracle SQL, ITIL
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
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
Delivery of a digital, non-profit neighborhood platform connecting older people with volunteers for practical support and social companionship. Responsibility for business analysis, requirements definition, platform evaluation and delivery of a locally scalable service solution, with a focus on AI-supported development, data-driven optimization and clear market positioning. Evaluation of several low-code/business platforms, including Odoo and Glide Apps, as well as assessment of monday.com as a CRM-related option, before selecting Glide Apps for implementation. Implementation of a non-native mobile and desktop application with Glide Apps and the website with WordPress. Responsibilities
- 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 key user journeys, business processes and operational workflows for older people, volunteers and Administration
- Translating business requirements into requirements for profiles, task management, coordination, communication and notifications
- Supporting the implementation of the non-native mobile and desktop application in Glide Apps and the website in WordPress
- Preparing future AI-supported functions such as intelligent matching and data-driven optimization
- Ensuring compliance with GDPR requirements Results
- Successfully delivered digital support platform connecting older people with volunteers
- Solid foundation for market positioning through market, target group and competitor analyses
- Implemented functional non-native mobile and desktop application with Glide Apps and accompanying website with WordPress
- Created 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
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
15
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
89%
Master's degree or higher
67%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
91%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Vienna 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 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 29 Oct 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 (91%)
- Professional Services (64%)
- Government and Administration (64%)
- Telecommunication (45%)
- Energy (36%)
- Healthcare (36%)
- Transportation (36%)
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 examines accuracy, completeness, consistency, timeliness, uniqueness, validity, and integrity across databases, files, APIs, and reporting systems. Strong quality work turns unreliable records into data that teams can trust for operations, analytics, and decisions.
Where It Is Used
Companies apply Data Quality controls wherever information moves between systems or supports critical processes.
- Profile customer, product, supplier, and financial data
- Detect duplicates, missing values, invalid formats, and conflicting records
- Validate data entering warehouses, lakes, CRM, and ERP systems
- Monitor quality for dashboards, regulatory reporting, and machine learning
Tools and Methods
Professionals may work with SQL, Python, spreadsheets, and database profiling tools alongside platforms such as Informatica Data Quality, Talend Data Quality, Ataccama, Great Expectations, Soda, and dbt tests. Their work often connects to ETL and ELT pipelines, data catalogs, master data management, observability, and governance processes. The right tool depends on data volume, architecture, controls, and ownership.
When Companies Need Support
Freelance expertise helps when quality problems slow migration projects, undermine reporting, or create repeated manual checks. Specialists can establish a baseline, define business rules with data owners, and embed automated checks without blocking delivery. In Vienna, remote collaboration often works well, while workshops with local stakeholders may benefit from on-site sessions and clear English or German communication.
Typical Deliverables
A focused engagement can produce a data quality assessment, profiling report, rule catalogue, validation framework, remediation plan, or monitoring dashboard. Experts may also map critical data elements, trace defects to source systems, configure alerts, and document ownership. They should connect every rule to a business purpose rather than treating clean data as an abstract technical goal.
What Strong Specialists Bring
The strongest professionals combine analytical discipline with practical knowledge of data platforms and business processes. They distinguish a genuine defect from an intentional exception, quantify impact without hiding assumptions, and explain findings to technical and non-technical teams. Look for clear acceptance criteria, reproducible checks, transparent documentation, and a plan for keeping quality stable after the engagement ends.
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 specific business purpose. Companies apply it to customer records, financial data, product information, operational systems, analytics, and machine learning inputs.
Data Quality focuses on whether data meets defined standards and business rules. Data governance defines ownership, policies, and decision rights, while data observability monitors the health and behavior of data pipelines and systems. These practices work best together.
A strong Data Quality specialist often brings SQL, Python, data profiling, ETL or ELT knowledge, and experience with warehouses or lakehouses. Useful adjacent skills include master data management, metadata, data catalogs, lineage, testing, governance, and stakeholder facilitation.
The required depth depends on the scope, risk, and number of source systems rather than on a fixed timeline. A focused profiling or rule-design engagement may need one specialist, while a company-wide program benefits from experience with governance, remediation, automation, and change management.
Yes. Data Quality work is often suitable for remote collaboration because profiling, rule design, documentation, and monitoring can be performed securely online. On-site workshops in Vienna can still help when experts need to align data owners, process teams, and technical stakeholders.
Data Quality work commonly uses SQL and Python together with tools such as Informatica Data Quality, Talend Data Quality, Ataccama, Great Expectations, Soda, and dbt tests. The best choice depends on the existing stack, data architecture, control requirements, and the team’s ability to maintain it.
Ask how the specialist defines quality dimensions, prioritizes critical data, traces defects to their source, and proves that checks work. A capable Data Quality professional can show clear rules, reproducible tests, useful documentation, and practical remediation steps rather than only reporting error counts.
A Data Quality freelancer should clarify the business outcomes, critical data elements, source systems, access constraints, owners, and acceptance criteria. They should also agree on how findings will be prioritized, who approves rules, and how ongoing monitoring will be maintained after delivery.
The average hourly rate of freelancers in Vienna, Austria who have used Data Quality in their recent projects is 105 €, which corresponds to a daily rate of about 844 € based on an 8-hour working day.
Of the freelancers in Vienna, Austria who have used Data Quality in their recent projects, 89% hold at least a Bachelor's degree and 67% 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 (91%), and French (36%).
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 (91%), and Professional Services (64%).
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 (82%).
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
