Data Quality Experts in Nuremberg
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Meet FRATCH Experts in Nuremberg, who have recently used Data Quality
Bhagyashree Mandlik
Last position:
Salesforce Administrator at gkk Frankfurt GmbH
- Translated complex operational requirements into scalable Salesforce configurations, automation flows and governance policies, ensuring clean and maintainable implementations
- Designed and optimised Flows to eliminate manual steps in key business processes, improving both performance and process compliance
- Managed user roles, permission sets and security settings for a multi-team Salesforce org; ensured data quality and system stability through structured governance practices
- Supported Marketing Cloud integrations and campaign automation workflows, collaborating with business teams on data-driven audience segmentation
- Coordinated release management and deployment activities with development teams, contributing to structured change control processes
Imane Habitou
Last position:
Assistant Manager People insights/Intelligence at adidas HQ
- Collected, analysed, and interpreted HR data from multiple systems
- Conducted ad-hoc people data analyses for stakeholders
- Supported Employee Listening and ensured actionable survey insights
- Built intuitive dashboards and visualizations in SuccessFactors, PowerBI, and Qualtrics
- Produced regular reports and dashboards on key HR metrics
- Contributed to people analytics projects as a junior team member
- Collaborated with HR Tech and Data teams to improve data quality
- Ensured ethical and governed data use with Legal, Data Privacy, Labour Relations and Works Council
Andreas Brachwitz
Last position:
Interim Manager - Project Excellence & Transformation at Brachwitz Interim Management
- Structured digitalization mandates for project businesses, developing governance frameworks, data models, and digital toolchain integration strategies for complex industrial environments
- Advised on data break resolution, standardized data flows, and end-to-end process harmonization across project management chains
- Developed knowledge retention strategies and digital tools to systematically capture and share organizational expertise
- Achieved active market positioning for project excellence and digitalization mandates in industrial environments (Siemens Energy, EFESO, Capgemini Engineering)
Benjamin Schötz
Last position:
CRM strategy & requirements analysis for marketing campaigns in the automotive sector at AUDI AG
- Development of strategies, processes and system requirements to further develop the lead and campaign streams of a Salesforce system for the subsystems Sales Cloud, Marketing Cloud and Analytics Cloud
- Gathering and creation of marketing strategy concepts for direct sales, focusing on lead generation for new and existing customers
- Analysis and optimization of existing and new contact points
- Derivation of system requirements and user tests for new developments
- Establishment of a requirements and implementation process for business needs at the interface between CRM strategy and marketing system operations
- Facilitation of ideation workshops with the project's stakeholders
- Mediation between business and technical project stakeholders
- Analysis and post-documentation of existing marketing strategies and the system functions already created for them
- Analysis of target groups and pre-system data quality as a foundation for new CRM strategies
- Consultation on content and asset strategy for marketing campaigns
- Definition of KPIs and other performance criteria for the system and CRM strategy
Tobias Von Dewitz
Last position:
Managing Partner at Unwritten GmbH
- Pioneer work in personalized AI: development of a framework for “Interactive Content” (RAG) for novels, lectures, expert debriefing
- Successful launch of Einbug, the Pantopia chatbot, with media resonance (SZ interview)
- Creation of compelling AI personalities: AI blog ([link]), 100% personalized learning environments, Perry Rhodan, and others.
Elnazossadat Hosseininia
Last position:
Data Analyst at Siemens Healthineers
- Developed KPI dashboards using Power BI and DAX for 4+ business units, improving reporting transparency and strategic decision support.
- Migrated enterprise finance data views into dbt models, implementing modular SQL transformations, version-controlled data pipelines, and automated documentation to create a scalable analytics layer.
- Built dimensional data models in Snowflake for enterprise finance data, enabling scalable forecasting and supporting executive decision-making.
- Designed end-to-end ETL/ELT pipelines using Snowflake and SAP HANA, integrating data from 3+ enterprise systems.
- Automated monthly reporting workflows using SQL and Power BI, delivering strong business impact by reducing manual effort by 80%.
- Collaborated with finance stakeholders to translate business requirements into analytical data models, supporting strategic decision-making cycles.
- Delivered ad-hoc financial reports using Power BI, reducing turnaround time by 60%.
- Implemented data validation logic in SQL, resolving 95% of recurring data quality issues.
Felix Kluge
Last position:
Senior Data Scientist at Novartis Pharma AG
- Led the implementation and validation of digital technology for real-world mobility assessment across clinical trials with high compliance rates
- Headed a cross-functional initiative that bridged clinical and data science teams, aligning digital technology integration with strategic goals
- Innovated algorithm development for sensor data analysis, leveraging time-series data to extract novel insights and improve predictive accuracy
- Designed and deployed predictive models using machine learning, integrating digital device data with clinical datasets
- Delivered strategic insights through the creation of dashboards and reports, ensuring effective data quality control and visualization for clinical trials
- Ensured reproducibility in data science workflows by establishing robust coding practices, comprehensive documentation, and version control
- Co-led exploratory statistical plans for digital biomarkers, advancing understanding and application of digital technologies in healthcare research
- Advised clinical teams as a subject-matter expert on digital technologies, fostering strategic integration and effective utilization in clinical trials
- Managed stakeholder engagement by collaborating with internal and external stakeholders, including contract research organizations and technology vendors
- Influenced decision-making by presenting analyses and findings to various management levels and stakeholders, emphasizing actionable insights and implications
- Disseminated knowledge through peer-reviewed publications
Discover over 15,000 top freelancers
Statistics of experts using Data Quality
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 16 years)
Position duration
3.4 years (Germany: 3 years)
Positions per freelancer
7 (Germany: 10)
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Automotive, Professional Services
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
83% (Germany: 68%)
Doctorate
17% (Germany: 11%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 98%)
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 Nuremberg 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 Nuremberg 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 quality basics
Data quality is the discipline of making data accurate, complete, consistent, timely, and usable. It sits between source systems, pipelines, and analytics, so teams can trust reports, automations, and machine learning inputs.
Typical work
- Define validation rules and acceptance checks
- Profile source data and find anomalies
- Set up monitoring for freshness, duplicates, and schema changes
- Improve data quality management for warehouses and BI layers
Tools and methods
Strong specialists work with SQL, Python, dbt, Spark, and orchestration tools, plus catalog and observability features where they exist. They also know how to write clear rules for data quality checks, document edge cases, and connect business meaning to technical validation.
When to bring in help
Companies usually hire freelance expertise when dashboards do not match source systems, integrations break after schema changes, or critical datasets need a cleanup before release. In Nuremberg, this is common in manufacturing, logistics, software, and shared-service teams that rely on reliable operational data.
What good specialists do
A strong professional does more than spot bad records. They trace defects to the source, design repeatable controls, and work with product, analytics, and engineering teams to prevent the same issue from coming back.
Local collaboration
For teams in Nuremberg, data quality work often fits well in a hybrid setup. Remote collaboration works for profiling, rule design, and documentation, while on-site time can help when source owners need fast alignment or when business users must sign off on definitions.
Frequently asked questions
Before you brief your next project: the most common questions about Data Quality.
Data Quality covers the rules, checks, and review steps that keep data reliable from source to report. It includes validation, profiling, reconciliation, and monitoring so teams can trust what they use in operations and analytics.
Data quality focuses on whether the data is fit for use; data governance focuses on ownership, policies, and decision rights. They work together, but a project can have governance documents and still fail if the checks and fixes are weak.
A Data Quality freelancer makes sense when you have recurring defects, a migration with unknown data issues, or a pipeline that needs stable checks before launch. It is also useful when your team needs a short burst of specialist help to define rules, test assumptions, or clean up a critical dataset.
A strong Data Quality specialist usually brings SQL, Python, data modeling, ETL or ELT knowledge, and a solid grasp of the business rules behind the data. Familiarity with dbt, Spark, orchestration tools, and catalog or observability features is often valuable too.
Data Quality is the broader practice of defining and enforcing what good data looks like, while data observability is more about detecting issues in pipelines and datasets. Many companies use both: observability to spot problems quickly, and quality rules to explain and prevent them.
For a focused Data Quality task, you may only need one strong specialist if the scope is clear and the data landscape is simple. For larger programs with many sources, multiple definitions, or sensitive reporting, a specialist who has worked across several systems is safer.
Yes, Data Quality work is often remote-friendly because much of it happens in SQL, documentation, and review sessions. On-site time in Nuremberg can still help when teams need quick access to source owners, business users, or local stakeholders who define the meaning of the data.
A strong Data Quality freelancer asks about source systems, ownership, failure modes, and how the business uses the data before proposing checks. Look for clear rule design, practical fixes, good documentation, and the ability to explain trade-offs without hiding behind vague technical language.
The average hourly rate of freelancers in Nuremberg, Germany who have used Data Quality in their recent projects is 91 €, which corresponds to a daily rate of about 730 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Data Quality in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Nuremberg, Germany who have used Data Quality in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 3.4 years.
The most common languages among freelancers in Nuremberg, Germany who have used Data Quality in their recent projects are German (100%), English (100%), and French (43%).
The most common industries among freelancers in Nuremberg, Germany who have used Data Quality in their recent projects are Information Technology (71%), Automotive (43%), and Professional Services (43%).
The most common business areas among freelancers in Nuremberg, Germany who have used Data Quality in their recent projects are Business Intelligence (86%), Information Technology (86%), 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.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Hanover Request a free demo
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Philipp Thomaschewski
FRATCH CEO
