Data Quality Experts in Zurich
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Meet FRATCH Experts in Zurich, who have recently used Data Quality
Gwang Jin Kim
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 Pytelkova
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 Elsen
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 Wydler
Last position:
IT Consultant at WYP-Consulting
Max Meinhardt
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 Schmidt
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 Kaczmarkiewicz
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 Dundurs
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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it covers
Data quality is the discipline of making data fit for use. It covers accuracy, completeness, consistency, timeliness, validity, and uniqueness across source systems, pipelines, and reporting layers. Strong work in data quality reduces bad decisions caused by missing fields, duplicate records, and mismatched definitions.
Typical work
- Define quality rules for master data, customer data, product data, and financial data
- Build checks for ETL and ELT pipelines, warehouse loads, and API feeds
- Set up monitoring, alerts, and issue triage for broken records
- Document business rules and data definitions for teams that depend on trusted data
Tools and stack
Data quality specialists often work with SQL, Python, dbt, Great Expectations, Soda, and tools inside cloud data stacks. They also understand lineage, catalog, metadata, and test automation. In Zurich, they are often brought into banking, insurance, and enterprise analytics projects where control, traceability, and clear documentation matter.
When to bring one in
Bring in freelance expertise when reports do not match, integrations keep failing, or the business cannot agree on one version of the truth. A good specialist finds the root cause, not just the symptom. They are also useful during migrations, new warehouse rollouts, and data governance initiatives that need practical execution.
What strong experts do
Strong professionals combine business judgment with technical discipline. They ask where the data comes from, how it should be used, and what failure looks like in daily operations. They write checks that are stable, easy to maintain, and tied to clear ownership, so quality work survives after delivery.
Why it matters
Data quality is not a one-time clean-up. It is an ongoing practice that supports analytics, operations, compliance, and customer-facing processes. Companies that invest in it get fewer surprises in dashboards, fewer manual corrections, and more confidence in decisions built on data.
Frequently asked questions
What clients ask us most about Data Quality — answered in short.
A strong Data Quality specialist finds where data breaks and puts controls in place to stop it from spreading. That usually includes rule design, validation checks, exception handling, and clear ownership for bad records. The goal is trusted data that teams can use without constant manual fixes.
Data Quality focuses on the condition of the data itself: is it complete, correct, and consistent. Data governance is broader and covers policies, ownership, definitions, and decision rights. In practice, the two work together, but quality work is more hands-on and technical.
Data Quality work often uses SQL, Python, dbt, Great Expectations, Soda, and data catalog tools. The exact stack depends on whether the environment is batch, streaming, cloud warehouse, or mixed. A good specialist can work inside the tools you already use and improve the checks without adding clutter.
A Data Quality professional usually brings strong SQL, data modeling, and pipeline troubleshooting skills. Familiarity with ETL and ELT flows, metadata, lineage, testing, and reporting tools is also valuable. In many projects, clear communication matters just as much as technical depth.
A Data Quality project can be small or complex, but the skill needed depends on how many systems are involved and how messy the data is. Simple rule checks may need only a focused specialist, while enterprise-wide programs need someone who can connect business definitions with technical controls. If the issue affects reporting, operations, or compliance, use someone proven.
Yes, Data Quality work is often well suited to remote collaboration because much of it happens in code, documentation, and review sessions. For Zurich-based companies, remote work is common, but on-site time can help when teams need to align on business definitions or source-system behavior. The best setup depends on access, security, and how many stakeholders need to be involved.
Ask for examples where Data Quality checks prevented real issues, not just where someone wrote tests. Look for clear root-cause thinking, maintainable rules, and evidence that the person can work with both business teams and technical systems. Strong specialists explain trade-offs plainly and leave behind checks that others can own.
Data Quality works best as part of a wider data effort that includes modeling, governance, monitoring, and ownership. If you only clean data once, problems return when new sources or processes go live. The strongest results come when quality checks are built into the pipeline and supported by the teams that create the data.
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
