Data Quality Experts in Zurich
matched in minutes with vetted, available freelancersHire experts who assess data accuracy, completeness and consistency, build validation rules, and improve master data processes across business systems. FRATCH matches you quickly with precise, vetted and available freelancers for your Data Quality project.
Meet FRATCH Experts in Zurich, who have recently used Data Quality
Gwang Jin K.
Last position:
Data Scientist / Applied AI, Automation & Data Systems Researcher at Independent
- Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
- Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
- Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
- Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
- Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
- Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
- Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
- Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations
Lenka P.
Last position:
Workday Technical Architect at Swisscom
(Project Assignment via consulting company HighPerfPeople)
- Lead the Workday Optimization Board, defining and presenting the Workday enhancement roadmap; drive prioritization based on stakeholder input, system analysis, and identified capability gaps.
- Challenge business requirements and proposed solutions to ensure alignment with long-term architecture principles, platform strategy and operational sustainability.
- Own the complete lifecycle of Workday enhancements from business discovery and solution design through implementation governance, testing strategy, deployment and stabilization.
- Act as functional/technical lead for key product areas including Peakon, Workday Learning, and Talent/Performance processes (e.g., year-end cycle, goal setting).
- Define and implement Workday branding and communication standards (templates, notification design, messaging structure) to improve consistency and employee experience.
- Build and enhance manager- and HR-facing tools (reports, dashboards, guidance/Journeys, process support assets) to reduce manual effort and improve decision-making.
- Translate complex system behavior, security/privacy constraints, and process implications into clear options for business stakeholders and leadership.
- Drive cross-functional collaboration with HR process owners, IT/security, and vendors to resolve issues and enable scalable solutions.
- Strong focus on adoption: ensure changes are usable, understandable, and actionable for employees, managers, and HR teams.
Monika E.
Last position:
Tax Specialist at Julius Bär / Bosshard & Partner
- Operational responsibility in tax relief at source
- Took over and stabilized a complex department during a project phase
- Analysis, review, and optimization of existing processes and workflows
- Updating and further development of the specialist manual
- Analysis and resolution of individual cases in an international tax environment
Peter W.
Last position:
IT Consultant at WYP-Consulting
Max M.
Last position:
Qualtrics CXM Solutions Expert at Freelancing
Support with the introduction and implementation of the Qualtrics XM platform in the consulting and service environment for customers, e.g. in the banking sector, focusing on data quality, parameterization, technical coordination, and implementation support
Technical validation of configurations, feedback flows, and reporting setups
Review and assurance of data quality (mapping, mandatory fields, regulatory-relevant fields)
Performance of UAT, functional tests, and data checks in collaboration with the department and IT
Coordination with stakeholders from consulting, service, compliance, IT, and product management
Clarification of technical requirements with the business side and derivation of adaptation requirements in the platform
Creation of documentation, test protocols, and quality-related artifacts
Support in the preparation of internal approvals and rollouts
Successful technical acceptance of the new feedback and reporting functionalities
Increase in data quality in the relevant fields (e.g., feedback classification, assignment to the consulting process)
Stable implementation of the platform in the consulting environment with clear handover to IT and specialist departments
Daniel S.
Last position:
Senior Manager at devpoint GmbH
- Design and co-implementation of an insolvency management platform in Germany for a client (based on an AI development tool)
- Concept, requirements engineering, and implementation support for a Dubai-based company to integrate processes into a CRM
- Business analysis and requirements engineering at Swisscom for integrations
- Integration of a new knowledge management system into the business processes at Swisscom AG – REST API definition
- Project manager, consultant, and sparring partner for the realignment/process digitalization at GIB Solutions AG
- Process designer and prototype for an AI-based real estate marketing system in Dubai
- Interim head of the ICT department at a telecom company, reorganizing and optimizing processes with a team of 5 at GIB Solutions AG
- Agile requirements engineer / external PO for a web-based solution for the German company DEHN AG
- Project management and consulting for the existing marketing and campaign planning solution at Swisscom AG
- Building the ALoHA nearshore offering at devpoint
Dariusz K.
Last position:
Business System Analyst at Zurich Insurance
Participating in a strategic enterprise project to introduce a centralized customer data platform ("Single Source of Truth") to eliminate redundant data storage and accelerate digital services
Analyzing and documenting business and system requirements for migrating large, heterogeneous data sets (ETL) and for continuous data synchronization between core systems and the new platform
Close collaboration with business units, data engineers and architects to gather and validate requirements, and to ensure data quality, consistency and regulatory compliance
Defining the migration strategy, specifying ETL processes and synchronization mechanisms, and documenting technical interfaces
Developing and executing test cases for migration and synchronization processes, and performing defect analysis to ensure data integrity and system stability
Technologies: SQL, Microsoft SQL Server, Azure DevOps, Confluence, UML, RESTful Services, Swagger, Postman
Viktors D.
Last position:
IT Consultant at Dundurs Consulting GmbH
- Supporting the implementation of a digitalization project in the area of nutrient balancing
- Analyzing complex business processes and interfaces to surrounding systems (data flows, dependencies)
- Designing processes for data retrieval, storage, and sharing, including plausibility checks and validation logic
- Creating and prioritizing user stories and guiding developers in agile sprints (clarifying acceptance criteria, conducting technical reviews)
- Supporting the Product Owner in defining functional and technical requirements
- Participating in the rollout process and delivering training for stakeholders
- Documenting processes, requirements, and interfaces (including records/changes for releases)
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
2.6 years
Positions per freelancer
8
Top business areas
Information Technology, Quality Assurance, Business Intelligence
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
80%
Master's degree or higher
60%
Doctorate
20%
Certifications per freelancer
3
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 Zurich 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 Zurich 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.
- Information Technology (88%)
- Banking and Finance (75%)
- Professional Services (75%)
- Government and Administration (38%)
- Chemical (25%)
- Education (25%)
- Insurance (25%)
- Manufacturing (25%)
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, validity, uniqueness and timeliness across databases, files, APIs and reporting systems. Specialists turn unclear data problems into measurable rules, ownership models and practical remediation work.
Where it is used
Companies rely on Data Quality work when information moves between departments, applications or external partners. Typical outcomes include:
- Clean customer, supplier and product records
- Validate data at ingestion and integration points
- Detect duplicates, missing values and invalid formats
- Improve reporting, analytics and machine learning inputs
Tools and connected skills
The work often combines SQL, Python, profiling tools and automated test frameworks. Depending on the landscape, specialists use Informatica, Talend, Ataccama, Great Expectations, dbt tests or cloud data services. They also understand data warehouses, ETL and ELT pipelines, APIs, metadata, lineage and master data management.
When companies need support
Freelance expertise is useful during migrations, acquisitions, CRM or ERP changes, data warehouse programmes and regulatory reporting initiatives. It also helps when teams cannot explain conflicting dashboards or when recurring manual corrections slow operations. In Zurich, remote collaboration can work well when documentation, access controls and communication with local stakeholders are clearly defined.
What strong specialists deliver
A capable professional begins with profiling and a shared definition of quality. They trace defects to their source rather than only correcting visible records, then design rules that teams can monitor over time. Strong deliverables may include a quality assessment, rule catalogue, exception workflow, remediation plan, dashboards and operating guidance.
How quality improvements last
Reliable Data Quality is not a one-off cleansing exercise. It needs accountable data owners, agreed thresholds, repeatable checks and feedback from the teams that create and use the information. The best specialists balance technical detail with business context, explain trade-offs clearly and leave behind controls that internal teams can maintain.
Frequently asked questions
What clients ask us most about Data Quality — answered in short.
Data Quality is used to determine whether information is accurate, complete, consistent, valid, unique and timely enough for a business purpose. Companies apply it to customer records, financial data, product catalogues, operational systems, analytics and machine learning workflows.
Data Quality is broader than data cleansing. Cleansing corrects, standardises or removes problematic records, while Data Quality also covers profiling, validation rules, monitoring, ownership, root-cause analysis and ongoing controls.
A strong Data Quality specialist often works with SQL, Python, data modelling, ETL or ELT pipelines and data warehouses. Knowledge of master data management, metadata, lineage, APIs, cloud services and business process analysis is also valuable.
The right level depends on the scope and risk of the work. Data Quality assessments may need a specialist who can profile data and define rules, while enterprise migrations or complex governance programmes call for someone who can coordinate systems, owners and remediation across the organisation.
Yes, Data Quality work is often suitable for remote collaboration because profiling, rule design and documentation can be performed securely online. On-site sessions in Zurich may still help with stakeholder workshops, access planning and agreement on definitions across business teams.
Ask for a clear method covering profiling, rule definition, defect prioritisation, root-cause analysis and monitoring. A credible Data Quality specialist can explain how they measure improvement, involve data owners and prevent the same defects from returning.
Data Quality projects may use SQL and Python alongside Informatica, Talend, Ataccama, Great Expectations or dbt tests. Tool choice should follow the data landscape, delivery model, security needs and the team’s ability to operate the resulting checks.
A Data Quality freelancer should clarify the business purpose, critical data elements, source systems, access rights, data owners and accepted quality thresholds. They should also agree how findings will be prioritised, who approves corrections and how controls will be maintained after delivery.
The average hourly rate of freelancers in Zurich, Switzerland who have used Data Quality in their recent projects is 109 €, which corresponds to a daily rate of about 872 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used Data Quality in their recent projects, 80% hold at least a Bachelor's degree, 60% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used Data Quality in their recent projects have 24 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers in Zurich, Switzerland who have used Data Quality in their recent projects are German (100%), English (100%), and French (50%).
The most common industries among freelancers in Zurich, Switzerland who have used Data Quality in their recent projects are Information Technology (88%), Banking and Finance (75%), and Professional Services (75%).
The most common business areas among freelancers in Zurich, Switzerland who have used Data Quality in their recent projects are Information Technology (88%), Quality Assurance (88%), and Business Intelligence (63%).
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:
Zurich 
Geneva Basel Bern Request a free demo
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Philipp Thomaschewski
FRATCH CEO
